modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
featherless-ai-quants/ankhamun-xxxI-Ixxx-GGUF
|
featherless-ai-quants
| 2024-11-10T19:43:09Z | 5 | 0 | null |
[
"gguf",
"text-generation",
"base_model:ankhamun/xxxI-Ixxx",
"base_model:quantized:ankhamun/xxxI-Ixxx",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-06T10:18:01Z |
---
base_model: ankhamun/xxxI-Ixxx
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# ankhamun/xxxI-Ixxx GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [ankhamun-xxxI-Ixxx-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/ankhamun-xxxI-Ixxx-GGUF/blob/main/ankhamun-xxxI-Ixxx-IQ4_XS.gguf) | 3761.66 MB |
| Q2_K | [ankhamun-xxxI-Ixxx-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/ankhamun-xxxI-Ixxx-GGUF/blob/main/ankhamun-xxxI-Ixxx-Q2_K.gguf) | 2593.27 MB |
| Q3_K_L | [ankhamun-xxxI-Ixxx-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/ankhamun-xxxI-Ixxx-GGUF/blob/main/ankhamun-xxxI-Ixxx-Q3_K_L.gguf) | 3644.97 MB |
| Q3_K_M | [ankhamun-xxxI-Ixxx-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/ankhamun-xxxI-Ixxx-GGUF/blob/main/ankhamun-xxxI-Ixxx-Q3_K_M.gguf) | 3355.97 MB |
| Q3_K_S | [ankhamun-xxxI-Ixxx-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/ankhamun-xxxI-Ixxx-GGUF/blob/main/ankhamun-xxxI-Ixxx-Q3_K_S.gguf) | 3017.97 MB |
| Q4_K_M | [ankhamun-xxxI-Ixxx-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/ankhamun-xxxI-Ixxx-GGUF/blob/main/ankhamun-xxxI-Ixxx-Q4_K_M.gguf) | 4166.07 MB |
| Q4_K_S | [ankhamun-xxxI-Ixxx-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/ankhamun-xxxI-Ixxx-GGUF/blob/main/ankhamun-xxxI-Ixxx-Q4_K_S.gguf) | 3948.57 MB |
| Q5_K_M | [ankhamun-xxxI-Ixxx-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/ankhamun-xxxI-Ixxx-GGUF/blob/main/ankhamun-xxxI-Ixxx-Q5_K_M.gguf) | 4893.69 MB |
| Q5_K_S | [ankhamun-xxxI-Ixxx-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/ankhamun-xxxI-Ixxx-GGUF/blob/main/ankhamun-xxxI-Ixxx-Q5_K_S.gguf) | 4766.19 MB |
| Q6_K | [ankhamun-xxxI-Ixxx-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/ankhamun-xxxI-Ixxx-GGUF/blob/main/ankhamun-xxxI-Ixxx-Q6_K.gguf) | 5666.80 MB |
| Q8_0 | [ankhamun-xxxI-Ixxx-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/ankhamun-xxxI-Ixxx-GGUF/blob/main/ankhamun-xxxI-Ixxx-Q8_0.gguf) | 7339.34 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/Muhammad2003-TriMistral-7B-TIES-GGUF
|
featherless-ai-quants
| 2024-11-10T19:43:05Z | 10 | 0 | null |
[
"gguf",
"text-generation",
"base_model:Muhammad2003/TriMistral-7B-TIES",
"base_model:quantized:Muhammad2003/TriMistral-7B-TIES",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-06T10:06:41Z |
---
base_model: Muhammad2003/TriMistral-7B-TIES
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# Muhammad2003/TriMistral-7B-TIES GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [Muhammad2003-TriMistral-7B-TIES-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/Muhammad2003-TriMistral-7B-TIES-GGUF/blob/main/Muhammad2003-TriMistral-7B-TIES-IQ4_XS.gguf) | 3761.66 MB |
| Q2_K | [Muhammad2003-TriMistral-7B-TIES-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/Muhammad2003-TriMistral-7B-TIES-GGUF/blob/main/Muhammad2003-TriMistral-7B-TIES-Q2_K.gguf) | 2593.27 MB |
| Q3_K_L | [Muhammad2003-TriMistral-7B-TIES-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/Muhammad2003-TriMistral-7B-TIES-GGUF/blob/main/Muhammad2003-TriMistral-7B-TIES-Q3_K_L.gguf) | 3644.97 MB |
| Q3_K_M | [Muhammad2003-TriMistral-7B-TIES-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/Muhammad2003-TriMistral-7B-TIES-GGUF/blob/main/Muhammad2003-TriMistral-7B-TIES-Q3_K_M.gguf) | 3355.97 MB |
| Q3_K_S | [Muhammad2003-TriMistral-7B-TIES-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/Muhammad2003-TriMistral-7B-TIES-GGUF/blob/main/Muhammad2003-TriMistral-7B-TIES-Q3_K_S.gguf) | 3017.97 MB |
| Q4_K_M | [Muhammad2003-TriMistral-7B-TIES-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/Muhammad2003-TriMistral-7B-TIES-GGUF/blob/main/Muhammad2003-TriMistral-7B-TIES-Q4_K_M.gguf) | 4166.07 MB |
| Q4_K_S | [Muhammad2003-TriMistral-7B-TIES-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/Muhammad2003-TriMistral-7B-TIES-GGUF/blob/main/Muhammad2003-TriMistral-7B-TIES-Q4_K_S.gguf) | 3948.57 MB |
| Q5_K_M | [Muhammad2003-TriMistral-7B-TIES-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/Muhammad2003-TriMistral-7B-TIES-GGUF/blob/main/Muhammad2003-TriMistral-7B-TIES-Q5_K_M.gguf) | 4893.69 MB |
| Q5_K_S | [Muhammad2003-TriMistral-7B-TIES-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/Muhammad2003-TriMistral-7B-TIES-GGUF/blob/main/Muhammad2003-TriMistral-7B-TIES-Q5_K_S.gguf) | 4766.19 MB |
| Q6_K | [Muhammad2003-TriMistral-7B-TIES-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/Muhammad2003-TriMistral-7B-TIES-GGUF/blob/main/Muhammad2003-TriMistral-7B-TIES-Q6_K.gguf) | 5666.80 MB |
| Q8_0 | [Muhammad2003-TriMistral-7B-TIES-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/Muhammad2003-TriMistral-7B-TIES-GGUF/blob/main/Muhammad2003-TriMistral-7B-TIES-Q8_0.gguf) | 7339.34 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/FPHam-L3-8B-Everything-COT-GGUF
|
featherless-ai-quants
| 2024-11-10T19:42:56Z | 32 | 0 | null |
[
"gguf",
"text-generation",
"base_model:FPHam/L3-8B-Everything-COT",
"base_model:quantized:FPHam/L3-8B-Everything-COT",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-06T09:26:52Z |
---
base_model: FPHam/L3-8B-Everything-COT
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# FPHam/L3-8B-Everything-COT GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [FPHam-L3-8B-Everything-COT-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/FPHam-L3-8B-Everything-COT-GGUF/blob/main/FPHam-L3-8B-Everything-COT-IQ4_XS.gguf) | 4276.62 MB |
| Q2_K | [FPHam-L3-8B-Everything-COT-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/FPHam-L3-8B-Everything-COT-GGUF/blob/main/FPHam-L3-8B-Everything-COT-Q2_K.gguf) | 3031.86 MB |
| Q3_K_L | [FPHam-L3-8B-Everything-COT-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/FPHam-L3-8B-Everything-COT-GGUF/blob/main/FPHam-L3-8B-Everything-COT-Q3_K_L.gguf) | 4121.74 MB |
| Q3_K_M | [FPHam-L3-8B-Everything-COT-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/FPHam-L3-8B-Everything-COT-GGUF/blob/main/FPHam-L3-8B-Everything-COT-Q3_K_M.gguf) | 3832.74 MB |
| Q3_K_S | [FPHam-L3-8B-Everything-COT-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/FPHam-L3-8B-Everything-COT-GGUF/blob/main/FPHam-L3-8B-Everything-COT-Q3_K_S.gguf) | 3494.74 MB |
| Q4_K_M | [FPHam-L3-8B-Everything-COT-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/FPHam-L3-8B-Everything-COT-GGUF/blob/main/FPHam-L3-8B-Everything-COT-Q4_K_M.gguf) | 4692.78 MB |
| Q4_K_S | [FPHam-L3-8B-Everything-COT-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/FPHam-L3-8B-Everything-COT-GGUF/blob/main/FPHam-L3-8B-Everything-COT-Q4_K_S.gguf) | 4475.28 MB |
| Q5_K_M | [FPHam-L3-8B-Everything-COT-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/FPHam-L3-8B-Everything-COT-GGUF/blob/main/FPHam-L3-8B-Everything-COT-Q5_K_M.gguf) | 5467.40 MB |
| Q5_K_S | [FPHam-L3-8B-Everything-COT-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/FPHam-L3-8B-Everything-COT-GGUF/blob/main/FPHam-L3-8B-Everything-COT-Q5_K_S.gguf) | 5339.90 MB |
| Q6_K | [FPHam-L3-8B-Everything-COT-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/FPHam-L3-8B-Everything-COT-GGUF/blob/main/FPHam-L3-8B-Everything-COT-Q6_K.gguf) | 6290.44 MB |
| Q8_0 | [FPHam-L3-8B-Everything-COT-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/FPHam-L3-8B-Everything-COT-GGUF/blob/main/FPHam-L3-8B-Everything-COT-Q8_0.gguf) | 8145.11 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/flammenai-flammen24-mistral-7B-GGUF
|
featherless-ai-quants
| 2024-11-10T19:42:49Z | 7 | 0 | null |
[
"gguf",
"text-generation",
"base_model:flammenai/flammen24-mistral-7B",
"base_model:quantized:flammenai/flammen24-mistral-7B",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-06T07:57:34Z |
---
base_model: flammenai/flammen24-mistral-7B
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# flammenai/flammen24-mistral-7B GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [flammenai-flammen24-mistral-7B-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/flammenai-flammen24-mistral-7B-GGUF/blob/main/flammenai-flammen24-mistral-7B-IQ4_XS.gguf) | 3761.66 MB |
| Q2_K | [flammenai-flammen24-mistral-7B-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/flammenai-flammen24-mistral-7B-GGUF/blob/main/flammenai-flammen24-mistral-7B-Q2_K.gguf) | 2593.27 MB |
| Q3_K_L | [flammenai-flammen24-mistral-7B-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/flammenai-flammen24-mistral-7B-GGUF/blob/main/flammenai-flammen24-mistral-7B-Q3_K_L.gguf) | 3644.97 MB |
| Q3_K_M | [flammenai-flammen24-mistral-7B-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/flammenai-flammen24-mistral-7B-GGUF/blob/main/flammenai-flammen24-mistral-7B-Q3_K_M.gguf) | 3355.97 MB |
| Q3_K_S | [flammenai-flammen24-mistral-7B-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/flammenai-flammen24-mistral-7B-GGUF/blob/main/flammenai-flammen24-mistral-7B-Q3_K_S.gguf) | 3017.97 MB |
| Q4_K_M | [flammenai-flammen24-mistral-7B-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/flammenai-flammen24-mistral-7B-GGUF/blob/main/flammenai-flammen24-mistral-7B-Q4_K_M.gguf) | 4166.07 MB |
| Q4_K_S | [flammenai-flammen24-mistral-7B-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/flammenai-flammen24-mistral-7B-GGUF/blob/main/flammenai-flammen24-mistral-7B-Q4_K_S.gguf) | 3948.57 MB |
| Q5_K_M | [flammenai-flammen24-mistral-7B-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/flammenai-flammen24-mistral-7B-GGUF/blob/main/flammenai-flammen24-mistral-7B-Q5_K_M.gguf) | 4893.69 MB |
| Q5_K_S | [flammenai-flammen24-mistral-7B-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/flammenai-flammen24-mistral-7B-GGUF/blob/main/flammenai-flammen24-mistral-7B-Q5_K_S.gguf) | 4766.19 MB |
| Q6_K | [flammenai-flammen24-mistral-7B-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/flammenai-flammen24-mistral-7B-GGUF/blob/main/flammenai-flammen24-mistral-7B-Q6_K.gguf) | 5666.80 MB |
| Q8_0 | [flammenai-flammen24-mistral-7B-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/flammenai-flammen24-mistral-7B-GGUF/blob/main/flammenai-flammen24-mistral-7B-Q8_0.gguf) | 7339.34 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/mlabonne-UltraMerge-7B-GGUF
|
featherless-ai-quants
| 2024-11-10T19:42:45Z | 10 | 0 | null |
[
"gguf",
"text-generation",
"base_model:mlabonne/UltraMerge-7B",
"base_model:quantized:mlabonne/UltraMerge-7B",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-06T07:31:33Z |
---
base_model: mlabonne/UltraMerge-7B
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# mlabonne/UltraMerge-7B GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [mlabonne-UltraMerge-7B-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/mlabonne-UltraMerge-7B-GGUF/blob/main/mlabonne-UltraMerge-7B-IQ4_XS.gguf) | 3761.66 MB |
| Q2_K | [mlabonne-UltraMerge-7B-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/mlabonne-UltraMerge-7B-GGUF/blob/main/mlabonne-UltraMerge-7B-Q2_K.gguf) | 2593.27 MB |
| Q3_K_L | [mlabonne-UltraMerge-7B-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/mlabonne-UltraMerge-7B-GGUF/blob/main/mlabonne-UltraMerge-7B-Q3_K_L.gguf) | 3644.97 MB |
| Q3_K_M | [mlabonne-UltraMerge-7B-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/mlabonne-UltraMerge-7B-GGUF/blob/main/mlabonne-UltraMerge-7B-Q3_K_M.gguf) | 3355.97 MB |
| Q3_K_S | [mlabonne-UltraMerge-7B-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/mlabonne-UltraMerge-7B-GGUF/blob/main/mlabonne-UltraMerge-7B-Q3_K_S.gguf) | 3017.97 MB |
| Q4_K_M | [mlabonne-UltraMerge-7B-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/mlabonne-UltraMerge-7B-GGUF/blob/main/mlabonne-UltraMerge-7B-Q4_K_M.gguf) | 4166.07 MB |
| Q4_K_S | [mlabonne-UltraMerge-7B-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/mlabonne-UltraMerge-7B-GGUF/blob/main/mlabonne-UltraMerge-7B-Q4_K_S.gguf) | 3948.57 MB |
| Q5_K_M | [mlabonne-UltraMerge-7B-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/mlabonne-UltraMerge-7B-GGUF/blob/main/mlabonne-UltraMerge-7B-Q5_K_M.gguf) | 4893.69 MB |
| Q5_K_S | [mlabonne-UltraMerge-7B-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/mlabonne-UltraMerge-7B-GGUF/blob/main/mlabonne-UltraMerge-7B-Q5_K_S.gguf) | 4766.19 MB |
| Q6_K | [mlabonne-UltraMerge-7B-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/mlabonne-UltraMerge-7B-GGUF/blob/main/mlabonne-UltraMerge-7B-Q6_K.gguf) | 5666.80 MB |
| Q8_0 | [mlabonne-UltraMerge-7B-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/mlabonne-UltraMerge-7B-GGUF/blob/main/mlabonne-UltraMerge-7B-Q8_0.gguf) | 7339.34 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/Locutusque-OpenCerebrum-2.0-7B-GGUF
|
featherless-ai-quants
| 2024-11-10T19:42:30Z | 8 | 0 | null |
[
"gguf",
"text-generation",
"base_model:Locutusque/OpenCerebrum-2.0-7B",
"base_model:quantized:Locutusque/OpenCerebrum-2.0-7B",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-06T05:33:25Z |
---
base_model: Locutusque/OpenCerebrum-2.0-7B
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# Locutusque/OpenCerebrum-2.0-7B GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [Locutusque-OpenCerebrum-2.0-7B-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-OpenCerebrum-2.0-7B-GGUF/blob/main/Locutusque-OpenCerebrum-2.0-7B-IQ4_XS.gguf) | 3761.66 MB |
| Q2_K | [Locutusque-OpenCerebrum-2.0-7B-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-OpenCerebrum-2.0-7B-GGUF/blob/main/Locutusque-OpenCerebrum-2.0-7B-Q2_K.gguf) | 2593.27 MB |
| Q3_K_L | [Locutusque-OpenCerebrum-2.0-7B-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-OpenCerebrum-2.0-7B-GGUF/blob/main/Locutusque-OpenCerebrum-2.0-7B-Q3_K_L.gguf) | 3644.97 MB |
| Q3_K_M | [Locutusque-OpenCerebrum-2.0-7B-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-OpenCerebrum-2.0-7B-GGUF/blob/main/Locutusque-OpenCerebrum-2.0-7B-Q3_K_M.gguf) | 3355.97 MB |
| Q3_K_S | [Locutusque-OpenCerebrum-2.0-7B-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-OpenCerebrum-2.0-7B-GGUF/blob/main/Locutusque-OpenCerebrum-2.0-7B-Q3_K_S.gguf) | 3017.97 MB |
| Q4_K_M | [Locutusque-OpenCerebrum-2.0-7B-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-OpenCerebrum-2.0-7B-GGUF/blob/main/Locutusque-OpenCerebrum-2.0-7B-Q4_K_M.gguf) | 4166.07 MB |
| Q4_K_S | [Locutusque-OpenCerebrum-2.0-7B-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-OpenCerebrum-2.0-7B-GGUF/blob/main/Locutusque-OpenCerebrum-2.0-7B-Q4_K_S.gguf) | 3948.57 MB |
| Q5_K_M | [Locutusque-OpenCerebrum-2.0-7B-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-OpenCerebrum-2.0-7B-GGUF/blob/main/Locutusque-OpenCerebrum-2.0-7B-Q5_K_M.gguf) | 4893.69 MB |
| Q5_K_S | [Locutusque-OpenCerebrum-2.0-7B-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-OpenCerebrum-2.0-7B-GGUF/blob/main/Locutusque-OpenCerebrum-2.0-7B-Q5_K_S.gguf) | 4766.19 MB |
| Q6_K | [Locutusque-OpenCerebrum-2.0-7B-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-OpenCerebrum-2.0-7B-GGUF/blob/main/Locutusque-OpenCerebrum-2.0-7B-Q6_K.gguf) | 5666.80 MB |
| Q8_0 | [Locutusque-OpenCerebrum-2.0-7B-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-OpenCerebrum-2.0-7B-GGUF/blob/main/Locutusque-OpenCerebrum-2.0-7B-Q8_0.gguf) | 7339.34 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/Danielbrdz-Barcenas-2x10.7b-Korean-GGUF
|
featherless-ai-quants
| 2024-11-10T19:42:03Z | 7 | 0 | null |
[
"gguf",
"text-generation",
"base_model:Danielbrdz/Barcenas-2x10.7b-Korean",
"base_model:quantized:Danielbrdz/Barcenas-2x10.7b-Korean",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-05T23:52:50Z |
---
base_model: Danielbrdz/Barcenas-2x10.7b-Korean
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# Danielbrdz/Barcenas-2x10.7b-Korean GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [Danielbrdz-Barcenas-2x10.7b-Korean-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/Danielbrdz-Barcenas-2x10.7b-Korean-GGUF/blob/main/Danielbrdz-Barcenas-2x10.7b-Korean-IQ4_XS.gguf) | 3761.66 MB |
| Q2_K | [Danielbrdz-Barcenas-2x10.7b-Korean-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/Danielbrdz-Barcenas-2x10.7b-Korean-GGUF/blob/main/Danielbrdz-Barcenas-2x10.7b-Korean-Q2_K.gguf) | 2593.27 MB |
| Q3_K_L | [Danielbrdz-Barcenas-2x10.7b-Korean-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/Danielbrdz-Barcenas-2x10.7b-Korean-GGUF/blob/main/Danielbrdz-Barcenas-2x10.7b-Korean-Q3_K_L.gguf) | 3644.97 MB |
| Q3_K_M | [Danielbrdz-Barcenas-2x10.7b-Korean-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/Danielbrdz-Barcenas-2x10.7b-Korean-GGUF/blob/main/Danielbrdz-Barcenas-2x10.7b-Korean-Q3_K_M.gguf) | 3355.97 MB |
| Q3_K_S | [Danielbrdz-Barcenas-2x10.7b-Korean-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/Danielbrdz-Barcenas-2x10.7b-Korean-GGUF/blob/main/Danielbrdz-Barcenas-2x10.7b-Korean-Q3_K_S.gguf) | 3017.97 MB |
| Q4_K_M | [Danielbrdz-Barcenas-2x10.7b-Korean-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/Danielbrdz-Barcenas-2x10.7b-Korean-GGUF/blob/main/Danielbrdz-Barcenas-2x10.7b-Korean-Q4_K_M.gguf) | 4166.07 MB |
| Q4_K_S | [Danielbrdz-Barcenas-2x10.7b-Korean-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/Danielbrdz-Barcenas-2x10.7b-Korean-GGUF/blob/main/Danielbrdz-Barcenas-2x10.7b-Korean-Q4_K_S.gguf) | 3948.57 MB |
| Q5_K_M | [Danielbrdz-Barcenas-2x10.7b-Korean-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/Danielbrdz-Barcenas-2x10.7b-Korean-GGUF/blob/main/Danielbrdz-Barcenas-2x10.7b-Korean-Q5_K_M.gguf) | 4893.69 MB |
| Q5_K_S | [Danielbrdz-Barcenas-2x10.7b-Korean-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/Danielbrdz-Barcenas-2x10.7b-Korean-GGUF/blob/main/Danielbrdz-Barcenas-2x10.7b-Korean-Q5_K_S.gguf) | 4766.19 MB |
| Q6_K | [Danielbrdz-Barcenas-2x10.7b-Korean-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/Danielbrdz-Barcenas-2x10.7b-Korean-GGUF/blob/main/Danielbrdz-Barcenas-2x10.7b-Korean-Q6_K.gguf) | 5666.80 MB |
| Q8_0 | [Danielbrdz-Barcenas-2x10.7b-Korean-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/Danielbrdz-Barcenas-2x10.7b-Korean-GGUF/blob/main/Danielbrdz-Barcenas-2x10.7b-Korean-Q8_0.gguf) | 7339.34 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-GGUF
|
featherless-ai-quants
| 2024-11-10T19:41:46Z | 41 | 0 | null |
[
"gguf",
"text-generation",
"base_model:grimjim/Llama-3.1-SuperNova-Lite-lorabilterated-8B",
"base_model:quantized:grimjim/Llama-3.1-SuperNova-Lite-lorabilterated-8B",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-05T22:25:46Z |
---
base_model: grimjim/Llama-3.1-SuperNova-Lite-lorabilterated-8B
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# grimjim/Llama-3.1-SuperNova-Lite-lorabilterated-8B GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-GGUF/blob/main/grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-IQ4_XS.gguf) | 4276.62 MB |
| Q2_K | [grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-GGUF/blob/main/grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-Q2_K.gguf) | 3031.86 MB |
| Q3_K_L | [grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-GGUF/blob/main/grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-Q3_K_L.gguf) | 4121.74 MB |
| Q3_K_M | [grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-GGUF/blob/main/grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-Q3_K_M.gguf) | 3832.74 MB |
| Q3_K_S | [grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-GGUF/blob/main/grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-Q3_K_S.gguf) | 3494.74 MB |
| Q4_K_M | [grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-GGUF/blob/main/grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-Q4_K_M.gguf) | 4692.78 MB |
| Q4_K_S | [grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-GGUF/blob/main/grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-Q4_K_S.gguf) | 4475.28 MB |
| Q5_K_M | [grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-GGUF/blob/main/grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-Q5_K_M.gguf) | 5467.40 MB |
| Q5_K_S | [grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-GGUF/blob/main/grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-Q5_K_S.gguf) | 5339.90 MB |
| Q6_K | [grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-GGUF/blob/main/grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-Q6_K.gguf) | 6290.44 MB |
| Q8_0 | [grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-GGUF/blob/main/grimjim-Llama-3.1-SuperNova-Lite-lorabilterated-8B-Q8_0.gguf) | 8145.11 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/nbeerbower-mistral-nemo-wissenschaft-12B-GGUF
|
featherless-ai-quants
| 2024-11-10T19:41:43Z | 18 | 0 | null |
[
"gguf",
"text-generation",
"base_model:nbeerbower/mistral-nemo-wissenschaft-12B",
"base_model:quantized:nbeerbower/mistral-nemo-wissenschaft-12B",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-05T22:03:56Z |
---
base_model: nbeerbower/mistral-nemo-wissenschaft-12B
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# nbeerbower/mistral-nemo-wissenschaft-12B GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [nbeerbower-mistral-nemo-wissenschaft-12B-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-mistral-nemo-wissenschaft-12B-GGUF/blob/main/nbeerbower-mistral-nemo-wissenschaft-12B-IQ4_XS.gguf) | 6485.04 MB |
| Q2_K | [nbeerbower-mistral-nemo-wissenschaft-12B-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-mistral-nemo-wissenschaft-12B-GGUF/blob/main/nbeerbower-mistral-nemo-wissenschaft-12B-Q2_K.gguf) | 4569.10 MB |
| Q3_K_L | [nbeerbower-mistral-nemo-wissenschaft-12B-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-mistral-nemo-wissenschaft-12B-GGUF/blob/main/nbeerbower-mistral-nemo-wissenschaft-12B-Q3_K_L.gguf) | 6257.54 MB |
| Q3_K_M | [nbeerbower-mistral-nemo-wissenschaft-12B-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-mistral-nemo-wissenschaft-12B-GGUF/blob/main/nbeerbower-mistral-nemo-wissenschaft-12B-Q3_K_M.gguf) | 5801.29 MB |
| Q3_K_S | [nbeerbower-mistral-nemo-wissenschaft-12B-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-mistral-nemo-wissenschaft-12B-GGUF/blob/main/nbeerbower-mistral-nemo-wissenschaft-12B-Q3_K_S.gguf) | 5277.85 MB |
| Q4_K_M | [nbeerbower-mistral-nemo-wissenschaft-12B-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-mistral-nemo-wissenschaft-12B-GGUF/blob/main/nbeerbower-mistral-nemo-wissenschaft-12B-Q4_K_M.gguf) | 7130.82 MB |
| Q4_K_S | [nbeerbower-mistral-nemo-wissenschaft-12B-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-mistral-nemo-wissenschaft-12B-GGUF/blob/main/nbeerbower-mistral-nemo-wissenschaft-12B-Q4_K_S.gguf) | 6790.35 MB |
| Q5_K_M | [nbeerbower-mistral-nemo-wissenschaft-12B-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-mistral-nemo-wissenschaft-12B-GGUF/blob/main/nbeerbower-mistral-nemo-wissenschaft-12B-Q5_K_M.gguf) | 8323.32 MB |
| Q5_K_S | [nbeerbower-mistral-nemo-wissenschaft-12B-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-mistral-nemo-wissenschaft-12B-GGUF/blob/main/nbeerbower-mistral-nemo-wissenschaft-12B-Q5_K_S.gguf) | 8124.10 MB |
| Q6_K | [nbeerbower-mistral-nemo-wissenschaft-12B-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-mistral-nemo-wissenschaft-12B-GGUF/blob/main/nbeerbower-mistral-nemo-wissenschaft-12B-Q6_K.gguf) | 9590.35 MB |
| Q8_0 | [nbeerbower-mistral-nemo-wissenschaft-12B-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-mistral-nemo-wissenschaft-12B-GGUF/blob/main/nbeerbower-mistral-nemo-wissenschaft-12B-Q8_0.gguf) | 12419.10 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/automerger-T3qm7xNeuralsirkrishna-7B-GGUF
|
featherless-ai-quants
| 2024-11-10T19:41:42Z | 6 | 0 | null |
[
"gguf",
"text-generation",
"base_model:automerger/T3qm7xNeuralsirkrishna-7B",
"base_model:quantized:automerger/T3qm7xNeuralsirkrishna-7B",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-05T21:42:12Z |
---
base_model: automerger/T3qm7xNeuralsirkrishna-7B
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# automerger/T3qm7xNeuralsirkrishna-7B GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [automerger-T3qm7xNeuralsirkrishna-7B-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/automerger-T3qm7xNeuralsirkrishna-7B-GGUF/blob/main/automerger-T3qm7xNeuralsirkrishna-7B-IQ4_XS.gguf) | 3761.66 MB |
| Q2_K | [automerger-T3qm7xNeuralsirkrishna-7B-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/automerger-T3qm7xNeuralsirkrishna-7B-GGUF/blob/main/automerger-T3qm7xNeuralsirkrishna-7B-Q2_K.gguf) | 2593.27 MB |
| Q3_K_L | [automerger-T3qm7xNeuralsirkrishna-7B-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/automerger-T3qm7xNeuralsirkrishna-7B-GGUF/blob/main/automerger-T3qm7xNeuralsirkrishna-7B-Q3_K_L.gguf) | 3644.97 MB |
| Q3_K_M | [automerger-T3qm7xNeuralsirkrishna-7B-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/automerger-T3qm7xNeuralsirkrishna-7B-GGUF/blob/main/automerger-T3qm7xNeuralsirkrishna-7B-Q3_K_M.gguf) | 3355.97 MB |
| Q3_K_S | [automerger-T3qm7xNeuralsirkrishna-7B-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/automerger-T3qm7xNeuralsirkrishna-7B-GGUF/blob/main/automerger-T3qm7xNeuralsirkrishna-7B-Q3_K_S.gguf) | 3017.97 MB |
| Q4_K_M | [automerger-T3qm7xNeuralsirkrishna-7B-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/automerger-T3qm7xNeuralsirkrishna-7B-GGUF/blob/main/automerger-T3qm7xNeuralsirkrishna-7B-Q4_K_M.gguf) | 4166.07 MB |
| Q4_K_S | [automerger-T3qm7xNeuralsirkrishna-7B-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/automerger-T3qm7xNeuralsirkrishna-7B-GGUF/blob/main/automerger-T3qm7xNeuralsirkrishna-7B-Q4_K_S.gguf) | 3948.57 MB |
| Q5_K_M | [automerger-T3qm7xNeuralsirkrishna-7B-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/automerger-T3qm7xNeuralsirkrishna-7B-GGUF/blob/main/automerger-T3qm7xNeuralsirkrishna-7B-Q5_K_M.gguf) | 4893.69 MB |
| Q5_K_S | [automerger-T3qm7xNeuralsirkrishna-7B-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/automerger-T3qm7xNeuralsirkrishna-7B-GGUF/blob/main/automerger-T3qm7xNeuralsirkrishna-7B-Q5_K_S.gguf) | 4766.19 MB |
| Q6_K | [automerger-T3qm7xNeuralsirkrishna-7B-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/automerger-T3qm7xNeuralsirkrishna-7B-GGUF/blob/main/automerger-T3qm7xNeuralsirkrishna-7B-Q6_K.gguf) | 5666.80 MB |
| Q8_0 | [automerger-T3qm7xNeuralsirkrishna-7B-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/automerger-T3qm7xNeuralsirkrishna-7B-GGUF/blob/main/automerger-T3qm7xNeuralsirkrishna-7B-Q8_0.gguf) | 7339.34 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/Liangmingxin-ThetaWave-7B-sft-GGUF
|
featherless-ai-quants
| 2024-11-10T19:41:40Z | 12 | 0 | null |
[
"gguf",
"text-generation",
"base_model:Liangmingxin/ThetaWave-7B-sft",
"base_model:quantized:Liangmingxin/ThetaWave-7B-sft",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-05T21:36:47Z |
---
base_model: Liangmingxin/ThetaWave-7B-sft
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# Liangmingxin/ThetaWave-7B-sft GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [Liangmingxin-ThetaWave-7B-sft-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/Liangmingxin-ThetaWave-7B-sft-GGUF/blob/main/Liangmingxin-ThetaWave-7B-sft-IQ4_XS.gguf) | 3761.66 MB |
| Q2_K | [Liangmingxin-ThetaWave-7B-sft-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/Liangmingxin-ThetaWave-7B-sft-GGUF/blob/main/Liangmingxin-ThetaWave-7B-sft-Q2_K.gguf) | 2593.27 MB |
| Q3_K_L | [Liangmingxin-ThetaWave-7B-sft-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/Liangmingxin-ThetaWave-7B-sft-GGUF/blob/main/Liangmingxin-ThetaWave-7B-sft-Q3_K_L.gguf) | 3644.97 MB |
| Q3_K_M | [Liangmingxin-ThetaWave-7B-sft-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/Liangmingxin-ThetaWave-7B-sft-GGUF/blob/main/Liangmingxin-ThetaWave-7B-sft-Q3_K_M.gguf) | 3355.97 MB |
| Q3_K_S | [Liangmingxin-ThetaWave-7B-sft-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/Liangmingxin-ThetaWave-7B-sft-GGUF/blob/main/Liangmingxin-ThetaWave-7B-sft-Q3_K_S.gguf) | 3017.97 MB |
| Q4_K_M | [Liangmingxin-ThetaWave-7B-sft-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/Liangmingxin-ThetaWave-7B-sft-GGUF/blob/main/Liangmingxin-ThetaWave-7B-sft-Q4_K_M.gguf) | 4166.07 MB |
| Q4_K_S | [Liangmingxin-ThetaWave-7B-sft-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/Liangmingxin-ThetaWave-7B-sft-GGUF/blob/main/Liangmingxin-ThetaWave-7B-sft-Q4_K_S.gguf) | 3948.57 MB |
| Q5_K_M | [Liangmingxin-ThetaWave-7B-sft-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/Liangmingxin-ThetaWave-7B-sft-GGUF/blob/main/Liangmingxin-ThetaWave-7B-sft-Q5_K_M.gguf) | 4893.69 MB |
| Q5_K_S | [Liangmingxin-ThetaWave-7B-sft-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/Liangmingxin-ThetaWave-7B-sft-GGUF/blob/main/Liangmingxin-ThetaWave-7B-sft-Q5_K_S.gguf) | 4766.19 MB |
| Q6_K | [Liangmingxin-ThetaWave-7B-sft-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/Liangmingxin-ThetaWave-7B-sft-GGUF/blob/main/Liangmingxin-ThetaWave-7B-sft-Q6_K.gguf) | 5666.80 MB |
| Q8_0 | [Liangmingxin-ThetaWave-7B-sft-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/Liangmingxin-ThetaWave-7B-sft-GGUF/blob/main/Liangmingxin-ThetaWave-7B-sft-Q8_0.gguf) | 7339.34 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/fusionbase-fusion-guide-12b-0.1-GGUF
|
featherless-ai-quants
| 2024-11-10T19:41:18Z | 12 | 0 | null |
[
"gguf",
"text-generation",
"base_model:fusionbase/fusion-guide-12b-0.1",
"base_model:quantized:fusionbase/fusion-guide-12b-0.1",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-05T18:01:06Z |
---
base_model: fusionbase/fusion-guide-12b-0.1
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# fusionbase/fusion-guide-12b-0.1 GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [fusionbase-fusion-guide-12b-0.1-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/fusionbase-fusion-guide-12b-0.1-GGUF/blob/main/fusionbase-fusion-guide-12b-0.1-IQ4_XS.gguf) | 6485.04 MB |
| Q2_K | [fusionbase-fusion-guide-12b-0.1-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/fusionbase-fusion-guide-12b-0.1-GGUF/blob/main/fusionbase-fusion-guide-12b-0.1-Q2_K.gguf) | 4569.10 MB |
| Q3_K_L | [fusionbase-fusion-guide-12b-0.1-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/fusionbase-fusion-guide-12b-0.1-GGUF/blob/main/fusionbase-fusion-guide-12b-0.1-Q3_K_L.gguf) | 6257.54 MB |
| Q3_K_M | [fusionbase-fusion-guide-12b-0.1-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/fusionbase-fusion-guide-12b-0.1-GGUF/blob/main/fusionbase-fusion-guide-12b-0.1-Q3_K_M.gguf) | 5801.29 MB |
| Q3_K_S | [fusionbase-fusion-guide-12b-0.1-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/fusionbase-fusion-guide-12b-0.1-GGUF/blob/main/fusionbase-fusion-guide-12b-0.1-Q3_K_S.gguf) | 5277.85 MB |
| Q4_K_M | [fusionbase-fusion-guide-12b-0.1-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/fusionbase-fusion-guide-12b-0.1-GGUF/blob/main/fusionbase-fusion-guide-12b-0.1-Q4_K_M.gguf) | 7130.82 MB |
| Q4_K_S | [fusionbase-fusion-guide-12b-0.1-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/fusionbase-fusion-guide-12b-0.1-GGUF/blob/main/fusionbase-fusion-guide-12b-0.1-Q4_K_S.gguf) | 6790.35 MB |
| Q5_K_M | [fusionbase-fusion-guide-12b-0.1-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/fusionbase-fusion-guide-12b-0.1-GGUF/blob/main/fusionbase-fusion-guide-12b-0.1-Q5_K_M.gguf) | 8323.32 MB |
| Q5_K_S | [fusionbase-fusion-guide-12b-0.1-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/fusionbase-fusion-guide-12b-0.1-GGUF/blob/main/fusionbase-fusion-guide-12b-0.1-Q5_K_S.gguf) | 8124.10 MB |
| Q6_K | [fusionbase-fusion-guide-12b-0.1-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/fusionbase-fusion-guide-12b-0.1-GGUF/blob/main/fusionbase-fusion-guide-12b-0.1-Q6_K.gguf) | 9590.35 MB |
| Q8_0 | [fusionbase-fusion-guide-12b-0.1-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/fusionbase-fusion-guide-12b-0.1-GGUF/blob/main/fusionbase-fusion-guide-12b-0.1-Q8_0.gguf) | 12419.10 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/Locutusque-Hyperion-1.5-Mistral-7B-GGUF
|
featherless-ai-quants
| 2024-11-10T19:41:07Z | 5 | 0 | null |
[
"gguf",
"text-generation",
"base_model:Locutusque/Hyperion-1.5-Mistral-7B",
"base_model:quantized:Locutusque/Hyperion-1.5-Mistral-7B",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-05T16:13:32Z |
---
base_model: Locutusque/Hyperion-1.5-Mistral-7B
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# Locutusque/Hyperion-1.5-Mistral-7B GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [Locutusque-Hyperion-1.5-Mistral-7B-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Hyperion-1.5-Mistral-7B-GGUF/blob/main/Locutusque-Hyperion-1.5-Mistral-7B-IQ4_XS.gguf) | 3761.66 MB |
| Q2_K | [Locutusque-Hyperion-1.5-Mistral-7B-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Hyperion-1.5-Mistral-7B-GGUF/blob/main/Locutusque-Hyperion-1.5-Mistral-7B-Q2_K.gguf) | 2593.27 MB |
| Q3_K_L | [Locutusque-Hyperion-1.5-Mistral-7B-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Hyperion-1.5-Mistral-7B-GGUF/blob/main/Locutusque-Hyperion-1.5-Mistral-7B-Q3_K_L.gguf) | 3644.97 MB |
| Q3_K_M | [Locutusque-Hyperion-1.5-Mistral-7B-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Hyperion-1.5-Mistral-7B-GGUF/blob/main/Locutusque-Hyperion-1.5-Mistral-7B-Q3_K_M.gguf) | 3355.97 MB |
| Q3_K_S | [Locutusque-Hyperion-1.5-Mistral-7B-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Hyperion-1.5-Mistral-7B-GGUF/blob/main/Locutusque-Hyperion-1.5-Mistral-7B-Q3_K_S.gguf) | 3017.97 MB |
| Q4_K_M | [Locutusque-Hyperion-1.5-Mistral-7B-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Hyperion-1.5-Mistral-7B-GGUF/blob/main/Locutusque-Hyperion-1.5-Mistral-7B-Q4_K_M.gguf) | 4166.07 MB |
| Q4_K_S | [Locutusque-Hyperion-1.5-Mistral-7B-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Hyperion-1.5-Mistral-7B-GGUF/blob/main/Locutusque-Hyperion-1.5-Mistral-7B-Q4_K_S.gguf) | 3948.57 MB |
| Q5_K_M | [Locutusque-Hyperion-1.5-Mistral-7B-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Hyperion-1.5-Mistral-7B-GGUF/blob/main/Locutusque-Hyperion-1.5-Mistral-7B-Q5_K_M.gguf) | 4893.69 MB |
| Q5_K_S | [Locutusque-Hyperion-1.5-Mistral-7B-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Hyperion-1.5-Mistral-7B-GGUF/blob/main/Locutusque-Hyperion-1.5-Mistral-7B-Q5_K_S.gguf) | 4766.19 MB |
| Q6_K | [Locutusque-Hyperion-1.5-Mistral-7B-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Hyperion-1.5-Mistral-7B-GGUF/blob/main/Locutusque-Hyperion-1.5-Mistral-7B-Q6_K.gguf) | 5666.80 MB |
| Q8_0 | [Locutusque-Hyperion-1.5-Mistral-7B-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Hyperion-1.5-Mistral-7B-GGUF/blob/main/Locutusque-Hyperion-1.5-Mistral-7B-Q8_0.gguf) | 7339.34 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/unsloth-Mistral-Nemo-Instruct-2407-GGUF
|
featherless-ai-quants
| 2024-11-10T19:40:56Z | 20 | 0 | null |
[
"gguf",
"text-generation",
"base_model:unsloth/Mistral-Nemo-Instruct-2407",
"base_model:quantized:unsloth/Mistral-Nemo-Instruct-2407",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-05T15:21:40Z |
---
base_model: unsloth/Mistral-Nemo-Instruct-2407
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# unsloth/Mistral-Nemo-Instruct-2407 GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [unsloth-Mistral-Nemo-Instruct-2407-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/unsloth-Mistral-Nemo-Instruct-2407-GGUF/blob/main/unsloth-Mistral-Nemo-Instruct-2407-IQ4_XS.gguf) | 6485.04 MB |
| Q2_K | [unsloth-Mistral-Nemo-Instruct-2407-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/unsloth-Mistral-Nemo-Instruct-2407-GGUF/blob/main/unsloth-Mistral-Nemo-Instruct-2407-Q2_K.gguf) | 4569.10 MB |
| Q3_K_L | [unsloth-Mistral-Nemo-Instruct-2407-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/unsloth-Mistral-Nemo-Instruct-2407-GGUF/blob/main/unsloth-Mistral-Nemo-Instruct-2407-Q3_K_L.gguf) | 6257.54 MB |
| Q3_K_M | [unsloth-Mistral-Nemo-Instruct-2407-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/unsloth-Mistral-Nemo-Instruct-2407-GGUF/blob/main/unsloth-Mistral-Nemo-Instruct-2407-Q3_K_M.gguf) | 5801.29 MB |
| Q3_K_S | [unsloth-Mistral-Nemo-Instruct-2407-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/unsloth-Mistral-Nemo-Instruct-2407-GGUF/blob/main/unsloth-Mistral-Nemo-Instruct-2407-Q3_K_S.gguf) | 5277.85 MB |
| Q4_K_M | [unsloth-Mistral-Nemo-Instruct-2407-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/unsloth-Mistral-Nemo-Instruct-2407-GGUF/blob/main/unsloth-Mistral-Nemo-Instruct-2407-Q4_K_M.gguf) | 7130.82 MB |
| Q4_K_S | [unsloth-Mistral-Nemo-Instruct-2407-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/unsloth-Mistral-Nemo-Instruct-2407-GGUF/blob/main/unsloth-Mistral-Nemo-Instruct-2407-Q4_K_S.gguf) | 6790.35 MB |
| Q5_K_M | [unsloth-Mistral-Nemo-Instruct-2407-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/unsloth-Mistral-Nemo-Instruct-2407-GGUF/blob/main/unsloth-Mistral-Nemo-Instruct-2407-Q5_K_M.gguf) | 8323.32 MB |
| Q5_K_S | [unsloth-Mistral-Nemo-Instruct-2407-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/unsloth-Mistral-Nemo-Instruct-2407-GGUF/blob/main/unsloth-Mistral-Nemo-Instruct-2407-Q5_K_S.gguf) | 8124.10 MB |
| Q6_K | [unsloth-Mistral-Nemo-Instruct-2407-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/unsloth-Mistral-Nemo-Instruct-2407-GGUF/blob/main/unsloth-Mistral-Nemo-Instruct-2407-Q6_K.gguf) | 9590.35 MB |
| Q8_0 | [unsloth-Mistral-Nemo-Instruct-2407-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/unsloth-Mistral-Nemo-Instruct-2407-GGUF/blob/main/unsloth-Mistral-Nemo-Instruct-2407-Q8_0.gguf) | 12419.10 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/picAIso-TARS-8B-GGUF
|
featherless-ai-quants
| 2024-11-10T19:40:53Z | 9 | 0 | null |
[
"gguf",
"text-generation",
"base_model:picAIso/TARS-8B",
"base_model:quantized:picAIso/TARS-8B",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-05T15:21:33Z |
---
base_model: picAIso/TARS-8B
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# picAIso/TARS-8B GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [picAIso-TARS-8B-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/picAIso-TARS-8B-GGUF/blob/main/picAIso-TARS-8B-IQ4_XS.gguf) | 4276.62 MB |
| Q2_K | [picAIso-TARS-8B-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/picAIso-TARS-8B-GGUF/blob/main/picAIso-TARS-8B-Q2_K.gguf) | 3031.86 MB |
| Q3_K_L | [picAIso-TARS-8B-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/picAIso-TARS-8B-GGUF/blob/main/picAIso-TARS-8B-Q3_K_L.gguf) | 4121.74 MB |
| Q3_K_M | [picAIso-TARS-8B-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/picAIso-TARS-8B-GGUF/blob/main/picAIso-TARS-8B-Q3_K_M.gguf) | 3832.74 MB |
| Q3_K_S | [picAIso-TARS-8B-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/picAIso-TARS-8B-GGUF/blob/main/picAIso-TARS-8B-Q3_K_S.gguf) | 3494.74 MB |
| Q4_K_M | [picAIso-TARS-8B-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/picAIso-TARS-8B-GGUF/blob/main/picAIso-TARS-8B-Q4_K_M.gguf) | 4692.78 MB |
| Q4_K_S | [picAIso-TARS-8B-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/picAIso-TARS-8B-GGUF/blob/main/picAIso-TARS-8B-Q4_K_S.gguf) | 4475.28 MB |
| Q5_K_M | [picAIso-TARS-8B-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/picAIso-TARS-8B-GGUF/blob/main/picAIso-TARS-8B-Q5_K_M.gguf) | 5467.40 MB |
| Q5_K_S | [picAIso-TARS-8B-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/picAIso-TARS-8B-GGUF/blob/main/picAIso-TARS-8B-Q5_K_S.gguf) | 5339.90 MB |
| Q6_K | [picAIso-TARS-8B-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/picAIso-TARS-8B-GGUF/blob/main/picAIso-TARS-8B-Q6_K.gguf) | 6290.44 MB |
| Q8_0 | [picAIso-TARS-8B-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/picAIso-TARS-8B-GGUF/blob/main/picAIso-TARS-8B-Q8_0.gguf) | 8145.11 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/maldv-badger-writer-llama-3-8b-GGUF
|
featherless-ai-quants
| 2024-11-10T19:40:49Z | 58 | 0 | null |
[
"gguf",
"text-generation",
"base_model:maldv/badger-writer-llama-3-8b",
"base_model:quantized:maldv/badger-writer-llama-3-8b",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-05T14:36:24Z |
---
base_model: maldv/badger-writer-llama-3-8b
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# maldv/badger-writer-llama-3-8b GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [maldv-badger-writer-llama-3-8b-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/maldv-badger-writer-llama-3-8b-GGUF/blob/main/maldv-badger-writer-llama-3-8b-IQ4_XS.gguf) | 4276.62 MB |
| Q2_K | [maldv-badger-writer-llama-3-8b-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/maldv-badger-writer-llama-3-8b-GGUF/blob/main/maldv-badger-writer-llama-3-8b-Q2_K.gguf) | 3031.86 MB |
| Q3_K_L | [maldv-badger-writer-llama-3-8b-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/maldv-badger-writer-llama-3-8b-GGUF/blob/main/maldv-badger-writer-llama-3-8b-Q3_K_L.gguf) | 4121.74 MB |
| Q3_K_M | [maldv-badger-writer-llama-3-8b-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/maldv-badger-writer-llama-3-8b-GGUF/blob/main/maldv-badger-writer-llama-3-8b-Q3_K_M.gguf) | 3832.74 MB |
| Q3_K_S | [maldv-badger-writer-llama-3-8b-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/maldv-badger-writer-llama-3-8b-GGUF/blob/main/maldv-badger-writer-llama-3-8b-Q3_K_S.gguf) | 3494.74 MB |
| Q4_K_M | [maldv-badger-writer-llama-3-8b-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/maldv-badger-writer-llama-3-8b-GGUF/blob/main/maldv-badger-writer-llama-3-8b-Q4_K_M.gguf) | 4692.78 MB |
| Q4_K_S | [maldv-badger-writer-llama-3-8b-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/maldv-badger-writer-llama-3-8b-GGUF/blob/main/maldv-badger-writer-llama-3-8b-Q4_K_S.gguf) | 4475.28 MB |
| Q5_K_M | [maldv-badger-writer-llama-3-8b-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/maldv-badger-writer-llama-3-8b-GGUF/blob/main/maldv-badger-writer-llama-3-8b-Q5_K_M.gguf) | 5467.40 MB |
| Q5_K_S | [maldv-badger-writer-llama-3-8b-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/maldv-badger-writer-llama-3-8b-GGUF/blob/main/maldv-badger-writer-llama-3-8b-Q5_K_S.gguf) | 5339.90 MB |
| Q6_K | [maldv-badger-writer-llama-3-8b-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/maldv-badger-writer-llama-3-8b-GGUF/blob/main/maldv-badger-writer-llama-3-8b-Q6_K.gguf) | 6290.44 MB |
| Q8_0 | [maldv-badger-writer-llama-3-8b-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/maldv-badger-writer-llama-3-8b-GGUF/blob/main/maldv-badger-writer-llama-3-8b-Q8_0.gguf) | 8145.11 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/4yo1-llama3-eng-ko-8b-sl3-GGUF
|
featherless-ai-quants
| 2024-11-10T19:40:44Z | 22 | 0 | null |
[
"gguf",
"text-generation",
"base_model:4yo1/llama3-eng-ko-8b-sl3",
"base_model:quantized:4yo1/llama3-eng-ko-8b-sl3",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-05T14:21:39Z |
---
base_model: 4yo1/llama3-eng-ko-8b-sl3
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# 4yo1/llama3-eng-ko-8b-sl3 GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [4yo1-llama3-eng-ko-8b-sl3-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/4yo1-llama3-eng-ko-8b-sl3-GGUF/blob/main/4yo1-llama3-eng-ko-8b-sl3-IQ4_XS.gguf) | 4276.62 MB |
| Q2_K | [4yo1-llama3-eng-ko-8b-sl3-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/4yo1-llama3-eng-ko-8b-sl3-GGUF/blob/main/4yo1-llama3-eng-ko-8b-sl3-Q2_K.gguf) | 3031.86 MB |
| Q3_K_L | [4yo1-llama3-eng-ko-8b-sl3-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/4yo1-llama3-eng-ko-8b-sl3-GGUF/blob/main/4yo1-llama3-eng-ko-8b-sl3-Q3_K_L.gguf) | 4121.74 MB |
| Q3_K_M | [4yo1-llama3-eng-ko-8b-sl3-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/4yo1-llama3-eng-ko-8b-sl3-GGUF/blob/main/4yo1-llama3-eng-ko-8b-sl3-Q3_K_M.gguf) | 3832.74 MB |
| Q3_K_S | [4yo1-llama3-eng-ko-8b-sl3-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/4yo1-llama3-eng-ko-8b-sl3-GGUF/blob/main/4yo1-llama3-eng-ko-8b-sl3-Q3_K_S.gguf) | 3494.74 MB |
| Q4_K_M | [4yo1-llama3-eng-ko-8b-sl3-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/4yo1-llama3-eng-ko-8b-sl3-GGUF/blob/main/4yo1-llama3-eng-ko-8b-sl3-Q4_K_M.gguf) | 4692.78 MB |
| Q4_K_S | [4yo1-llama3-eng-ko-8b-sl3-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/4yo1-llama3-eng-ko-8b-sl3-GGUF/blob/main/4yo1-llama3-eng-ko-8b-sl3-Q4_K_S.gguf) | 4475.28 MB |
| Q5_K_M | [4yo1-llama3-eng-ko-8b-sl3-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/4yo1-llama3-eng-ko-8b-sl3-GGUF/blob/main/4yo1-llama3-eng-ko-8b-sl3-Q5_K_M.gguf) | 5467.40 MB |
| Q5_K_S | [4yo1-llama3-eng-ko-8b-sl3-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/4yo1-llama3-eng-ko-8b-sl3-GGUF/blob/main/4yo1-llama3-eng-ko-8b-sl3-Q5_K_S.gguf) | 5339.90 MB |
| Q6_K | [4yo1-llama3-eng-ko-8b-sl3-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/4yo1-llama3-eng-ko-8b-sl3-GGUF/blob/main/4yo1-llama3-eng-ko-8b-sl3-Q6_K.gguf) | 6290.44 MB |
| Q8_0 | [4yo1-llama3-eng-ko-8b-sl3-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/4yo1-llama3-eng-ko-8b-sl3-GGUF/blob/main/4yo1-llama3-eng-ko-8b-sl3-Q8_0.gguf) | 8145.11 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-GGUF
|
featherless-ai-quants
| 2024-11-10T19:40:42Z | 48 | 0 | null |
[
"gguf",
"text-generation",
"base_model:nasiruddin15/Mistral-dolphin-2.8-grok-instract-2-7B-slerp",
"base_model:quantized:nasiruddin15/Mistral-dolphin-2.8-grok-instract-2-7B-slerp",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-05T14:15:38Z |
---
base_model: nasiruddin15/Mistral-dolphin-2.8-grok-instract-2-7B-slerp
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# nasiruddin15/Mistral-dolphin-2.8-grok-instract-2-7B-slerp GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-GGUF/blob/main/nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-IQ4_XS.gguf) | 3761.66 MB |
| Q2_K | [nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-GGUF/blob/main/nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-Q2_K.gguf) | 2593.27 MB |
| Q3_K_L | [nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-GGUF/blob/main/nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-Q3_K_L.gguf) | 3644.97 MB |
| Q3_K_M | [nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-GGUF/blob/main/nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-Q3_K_M.gguf) | 3355.97 MB |
| Q3_K_S | [nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-GGUF/blob/main/nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-Q3_K_S.gguf) | 3017.97 MB |
| Q4_K_M | [nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-GGUF/blob/main/nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-Q4_K_M.gguf) | 4166.07 MB |
| Q4_K_S | [nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-GGUF/blob/main/nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-Q4_K_S.gguf) | 3948.57 MB |
| Q5_K_M | [nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-GGUF/blob/main/nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-Q5_K_M.gguf) | 4893.69 MB |
| Q5_K_S | [nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-GGUF/blob/main/nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-Q5_K_S.gguf) | 4766.19 MB |
| Q6_K | [nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-GGUF/blob/main/nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-Q6_K.gguf) | 5666.80 MB |
| Q8_0 | [nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-GGUF/blob/main/nasiruddin15-Mistral-dolphin-2.8-grok-instract-2-7B-slerp-Q8_0.gguf) | 7339.34 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/royallab-MN-LooseCannon-12B-v2-GGUF
|
featherless-ai-quants
| 2024-11-10T19:40:30Z | 126 | 0 | null |
[
"gguf",
"text-generation",
"base_model:royallab/MN-LooseCannon-12B-v2",
"base_model:quantized:royallab/MN-LooseCannon-12B-v2",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-05T13:28:34Z |
---
base_model: royallab/MN-LooseCannon-12B-v2
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# royallab/MN-LooseCannon-12B-v2 GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [royallab-MN-LooseCannon-12B-v2-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/royallab-MN-LooseCannon-12B-v2-GGUF/blob/main/royallab-MN-LooseCannon-12B-v2-IQ4_XS.gguf) | 6485.04 MB |
| Q2_K | [royallab-MN-LooseCannon-12B-v2-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/royallab-MN-LooseCannon-12B-v2-GGUF/blob/main/royallab-MN-LooseCannon-12B-v2-Q2_K.gguf) | 4569.10 MB |
| Q3_K_L | [royallab-MN-LooseCannon-12B-v2-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/royallab-MN-LooseCannon-12B-v2-GGUF/blob/main/royallab-MN-LooseCannon-12B-v2-Q3_K_L.gguf) | 6257.54 MB |
| Q3_K_M | [royallab-MN-LooseCannon-12B-v2-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/royallab-MN-LooseCannon-12B-v2-GGUF/blob/main/royallab-MN-LooseCannon-12B-v2-Q3_K_M.gguf) | 5801.29 MB |
| Q3_K_S | [royallab-MN-LooseCannon-12B-v2-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/royallab-MN-LooseCannon-12B-v2-GGUF/blob/main/royallab-MN-LooseCannon-12B-v2-Q3_K_S.gguf) | 5277.85 MB |
| Q4_K_M | [royallab-MN-LooseCannon-12B-v2-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/royallab-MN-LooseCannon-12B-v2-GGUF/blob/main/royallab-MN-LooseCannon-12B-v2-Q4_K_M.gguf) | 7130.82 MB |
| Q4_K_S | [royallab-MN-LooseCannon-12B-v2-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/royallab-MN-LooseCannon-12B-v2-GGUF/blob/main/royallab-MN-LooseCannon-12B-v2-Q4_K_S.gguf) | 6790.35 MB |
| Q5_K_M | [royallab-MN-LooseCannon-12B-v2-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/royallab-MN-LooseCannon-12B-v2-GGUF/blob/main/royallab-MN-LooseCannon-12B-v2-Q5_K_M.gguf) | 8323.32 MB |
| Q5_K_S | [royallab-MN-LooseCannon-12B-v2-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/royallab-MN-LooseCannon-12B-v2-GGUF/blob/main/royallab-MN-LooseCannon-12B-v2-Q5_K_S.gguf) | 8124.10 MB |
| Q6_K | [royallab-MN-LooseCannon-12B-v2-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/royallab-MN-LooseCannon-12B-v2-GGUF/blob/main/royallab-MN-LooseCannon-12B-v2-Q6_K.gguf) | 9590.35 MB |
| Q8_0 | [royallab-MN-LooseCannon-12B-v2-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/royallab-MN-LooseCannon-12B-v2-GGUF/blob/main/royallab-MN-LooseCannon-12B-v2-Q8_0.gguf) | 12419.10 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/hivaze-ParaLex-Llama-3-8B-SFT-GGUF
|
featherless-ai-quants
| 2024-11-10T19:40:15Z | 17 | 0 | null |
[
"gguf",
"text-generation",
"base_model:hivaze/ParaLex-Llama-3-8B-SFT",
"base_model:quantized:hivaze/ParaLex-Llama-3-8B-SFT",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-05T12:19:26Z |
---
base_model: hivaze/ParaLex-Llama-3-8B-SFT
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# hivaze/ParaLex-Llama-3-8B-SFT GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [hivaze-ParaLex-Llama-3-8B-SFT-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/hivaze-ParaLex-Llama-3-8B-SFT-GGUF/blob/main/hivaze-ParaLex-Llama-3-8B-SFT-IQ4_XS.gguf) | 4276.62 MB |
| Q2_K | [hivaze-ParaLex-Llama-3-8B-SFT-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/hivaze-ParaLex-Llama-3-8B-SFT-GGUF/blob/main/hivaze-ParaLex-Llama-3-8B-SFT-Q2_K.gguf) | 3031.86 MB |
| Q3_K_L | [hivaze-ParaLex-Llama-3-8B-SFT-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/hivaze-ParaLex-Llama-3-8B-SFT-GGUF/blob/main/hivaze-ParaLex-Llama-3-8B-SFT-Q3_K_L.gguf) | 4121.74 MB |
| Q3_K_M | [hivaze-ParaLex-Llama-3-8B-SFT-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/hivaze-ParaLex-Llama-3-8B-SFT-GGUF/blob/main/hivaze-ParaLex-Llama-3-8B-SFT-Q3_K_M.gguf) | 3832.74 MB |
| Q3_K_S | [hivaze-ParaLex-Llama-3-8B-SFT-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/hivaze-ParaLex-Llama-3-8B-SFT-GGUF/blob/main/hivaze-ParaLex-Llama-3-8B-SFT-Q3_K_S.gguf) | 3494.74 MB |
| Q4_K_M | [hivaze-ParaLex-Llama-3-8B-SFT-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/hivaze-ParaLex-Llama-3-8B-SFT-GGUF/blob/main/hivaze-ParaLex-Llama-3-8B-SFT-Q4_K_M.gguf) | 4692.78 MB |
| Q4_K_S | [hivaze-ParaLex-Llama-3-8B-SFT-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/hivaze-ParaLex-Llama-3-8B-SFT-GGUF/blob/main/hivaze-ParaLex-Llama-3-8B-SFT-Q4_K_S.gguf) | 4475.28 MB |
| Q5_K_M | [hivaze-ParaLex-Llama-3-8B-SFT-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/hivaze-ParaLex-Llama-3-8B-SFT-GGUF/blob/main/hivaze-ParaLex-Llama-3-8B-SFT-Q5_K_M.gguf) | 5467.40 MB |
| Q5_K_S | [hivaze-ParaLex-Llama-3-8B-SFT-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/hivaze-ParaLex-Llama-3-8B-SFT-GGUF/blob/main/hivaze-ParaLex-Llama-3-8B-SFT-Q5_K_S.gguf) | 5339.90 MB |
| Q6_K | [hivaze-ParaLex-Llama-3-8B-SFT-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/hivaze-ParaLex-Llama-3-8B-SFT-GGUF/blob/main/hivaze-ParaLex-Llama-3-8B-SFT-Q6_K.gguf) | 6290.44 MB |
| Q8_0 | [hivaze-ParaLex-Llama-3-8B-SFT-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/hivaze-ParaLex-Llama-3-8B-SFT-GGUF/blob/main/hivaze-ParaLex-Llama-3-8B-SFT-Q8_0.gguf) | 8145.11 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-GGUF
|
featherless-ai-quants
| 2024-11-10T19:40:06Z | 20 | 0 | null |
[
"gguf",
"text-generation",
"base_model:lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half",
"base_model:quantized:lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-05T11:54:57Z |
---
base_model: lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-GGUF/blob/main/lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-IQ4_XS.gguf) | 4276.62 MB |
| Q2_K | [lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-GGUF/blob/main/lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-Q2_K.gguf) | 3031.86 MB |
| Q3_K_L | [lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-GGUF/blob/main/lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-Q3_K_L.gguf) | 4121.74 MB |
| Q3_K_M | [lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-GGUF/blob/main/lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-Q3_K_M.gguf) | 3832.74 MB |
| Q3_K_S | [lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-GGUF/blob/main/lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-Q3_K_S.gguf) | 3494.74 MB |
| Q4_K_M | [lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-GGUF/blob/main/lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-Q4_K_M.gguf) | 4692.78 MB |
| Q4_K_S | [lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-GGUF/blob/main/lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-Q4_K_S.gguf) | 4475.28 MB |
| Q5_K_M | [lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-GGUF/blob/main/lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-Q5_K_M.gguf) | 5467.40 MB |
| Q5_K_S | [lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-GGUF/blob/main/lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-Q5_K_S.gguf) | 5339.90 MB |
| Q6_K | [lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-GGUF/blob/main/lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-Q6_K.gguf) | 6290.44 MB |
| Q8_0 | [lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-GGUF/blob/main/lightblue-suzume-llama-3-8B-multilingual-orpo-borda-half-Q8_0.gguf) | 8145.11 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/Darkknight535-OpenCrystal-15B-L3-v3-GGUF
|
featherless-ai-quants
| 2024-11-10T19:40:02Z | 73 | 0 | null |
[
"gguf",
"text-generation",
"base_model:Darkknight535/OpenCrystal-15B-L3-v3",
"base_model:quantized:Darkknight535/OpenCrystal-15B-L3-v3",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-05T11:44:58Z |
---
base_model: Darkknight535/OpenCrystal-15B-L3-v3
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# Darkknight535/OpenCrystal-15B-L3-v3 GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [Darkknight535-OpenCrystal-15B-L3-v3-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/Darkknight535-OpenCrystal-15B-L3-v3-GGUF/blob/main/Darkknight535-OpenCrystal-15B-L3-v3-IQ4_XS.gguf) | 7868.64 MB |
| Q2_K | [Darkknight535-OpenCrystal-15B-L3-v3-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/Darkknight535-OpenCrystal-15B-L3-v3-GGUF/blob/main/Darkknight535-OpenCrystal-15B-L3-v3-Q2_K.gguf) | 5480.87 MB |
| Q3_K_L | [Darkknight535-OpenCrystal-15B-L3-v3-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/Darkknight535-OpenCrystal-15B-L3-v3-GGUF/blob/main/Darkknight535-OpenCrystal-15B-L3-v3-Q3_K_L.gguf) | 7609.76 MB |
| Q3_K_M | [Darkknight535-OpenCrystal-15B-L3-v3-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/Darkknight535-OpenCrystal-15B-L3-v3-GGUF/blob/main/Darkknight535-OpenCrystal-15B-L3-v3-Q3_K_M.gguf) | 7030.76 MB |
| Q3_K_S | [Darkknight535-OpenCrystal-15B-L3-v3-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/Darkknight535-OpenCrystal-15B-L3-v3-GGUF/blob/main/Darkknight535-OpenCrystal-15B-L3-v3-Q3_K_S.gguf) | 6355.76 MB |
| Q4_K_M | [Darkknight535-OpenCrystal-15B-L3-v3-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/Darkknight535-OpenCrystal-15B-L3-v3-GGUF/blob/main/Darkknight535-OpenCrystal-15B-L3-v3-Q4_K_M.gguf) | 8685.29 MB |
| Q4_K_S | [Darkknight535-OpenCrystal-15B-L3-v3-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/Darkknight535-OpenCrystal-15B-L3-v3-GGUF/blob/main/Darkknight535-OpenCrystal-15B-L3-v3-Q4_K_S.gguf) | 8248.29 MB |
| Q5_K_M | [Darkknight535-OpenCrystal-15B-L3-v3-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/Darkknight535-OpenCrystal-15B-L3-v3-GGUF/blob/main/Darkknight535-OpenCrystal-15B-L3-v3-Q5_K_M.gguf) | 10171.92 MB |
| Q5_K_S | [Darkknight535-OpenCrystal-15B-L3-v3-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/Darkknight535-OpenCrystal-15B-L3-v3-GGUF/blob/main/Darkknight535-OpenCrystal-15B-L3-v3-Q5_K_S.gguf) | 9916.92 MB |
| Q6_K | [Darkknight535-OpenCrystal-15B-L3-v3-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/Darkknight535-OpenCrystal-15B-L3-v3-GGUF/blob/main/Darkknight535-OpenCrystal-15B-L3-v3-Q6_K.gguf) | 11751.46 MB |
| Q8_0 | [Darkknight535-OpenCrystal-15B-L3-v3-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/Darkknight535-OpenCrystal-15B-L3-v3-GGUF/blob/main/Darkknight535-OpenCrystal-15B-L3-v3-Q8_0.gguf) | 15218.13 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-GGUF
|
featherless-ai-quants
| 2024-11-10T19:39:55Z | 25 | 0 | null |
[
"gguf",
"text-generation",
"base_model:nbeerbower/Lyra-Gutenberg-mistral-nemo-12B",
"base_model:quantized:nbeerbower/Lyra-Gutenberg-mistral-nemo-12B",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-05T10:47:19Z |
---
base_model: nbeerbower/Lyra-Gutenberg-mistral-nemo-12B
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# nbeerbower/Lyra-Gutenberg-mistral-nemo-12B GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-GGUF/blob/main/nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-IQ4_XS.gguf) | 6485.04 MB |
| Q2_K | [nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-GGUF/blob/main/nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-Q2_K.gguf) | 4569.10 MB |
| Q3_K_L | [nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-GGUF/blob/main/nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-Q3_K_L.gguf) | 6257.54 MB |
| Q3_K_M | [nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-GGUF/blob/main/nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-Q3_K_M.gguf) | 5801.29 MB |
| Q3_K_S | [nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-GGUF/blob/main/nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-Q3_K_S.gguf) | 5277.85 MB |
| Q4_K_M | [nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-GGUF/blob/main/nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-Q4_K_M.gguf) | 7130.82 MB |
| Q4_K_S | [nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-GGUF/blob/main/nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-Q4_K_S.gguf) | 6790.35 MB |
| Q5_K_M | [nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-GGUF/blob/main/nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-Q5_K_M.gguf) | 8323.32 MB |
| Q5_K_S | [nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-GGUF/blob/main/nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-Q5_K_S.gguf) | 8124.10 MB |
| Q6_K | [nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-GGUF/blob/main/nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-Q6_K.gguf) | 9590.35 MB |
| Q8_0 | [nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-GGUF/blob/main/nbeerbower-Lyra-Gutenberg-mistral-nemo-12B-Q8_0.gguf) | 12419.10 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/KOCDIGITAL-Kocdigital-LLM-8b-v0.1-GGUF
|
featherless-ai-quants
| 2024-11-10T19:39:48Z | 19 | 0 | null |
[
"gguf",
"text-generation",
"base_model:KOCDIGITAL/Kocdigital-LLM-8b-v0.1",
"base_model:quantized:KOCDIGITAL/Kocdigital-LLM-8b-v0.1",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-05T10:35:23Z |
---
base_model: KOCDIGITAL/Kocdigital-LLM-8b-v0.1
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# KOCDIGITAL/Kocdigital-LLM-8b-v0.1 GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [KOCDIGITAL-Kocdigital-LLM-8b-v0.1-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/KOCDIGITAL-Kocdigital-LLM-8b-v0.1-GGUF/blob/main/KOCDIGITAL-Kocdigital-LLM-8b-v0.1-IQ4_XS.gguf) | 4276.62 MB |
| Q2_K | [KOCDIGITAL-Kocdigital-LLM-8b-v0.1-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/KOCDIGITAL-Kocdigital-LLM-8b-v0.1-GGUF/blob/main/KOCDIGITAL-Kocdigital-LLM-8b-v0.1-Q2_K.gguf) | 3031.86 MB |
| Q3_K_L | [KOCDIGITAL-Kocdigital-LLM-8b-v0.1-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/KOCDIGITAL-Kocdigital-LLM-8b-v0.1-GGUF/blob/main/KOCDIGITAL-Kocdigital-LLM-8b-v0.1-Q3_K_L.gguf) | 4121.74 MB |
| Q3_K_M | [KOCDIGITAL-Kocdigital-LLM-8b-v0.1-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/KOCDIGITAL-Kocdigital-LLM-8b-v0.1-GGUF/blob/main/KOCDIGITAL-Kocdigital-LLM-8b-v0.1-Q3_K_M.gguf) | 3832.74 MB |
| Q3_K_S | [KOCDIGITAL-Kocdigital-LLM-8b-v0.1-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/KOCDIGITAL-Kocdigital-LLM-8b-v0.1-GGUF/blob/main/KOCDIGITAL-Kocdigital-LLM-8b-v0.1-Q3_K_S.gguf) | 3494.74 MB |
| Q4_K_M | [KOCDIGITAL-Kocdigital-LLM-8b-v0.1-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/KOCDIGITAL-Kocdigital-LLM-8b-v0.1-GGUF/blob/main/KOCDIGITAL-Kocdigital-LLM-8b-v0.1-Q4_K_M.gguf) | 4692.78 MB |
| Q4_K_S | [KOCDIGITAL-Kocdigital-LLM-8b-v0.1-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/KOCDIGITAL-Kocdigital-LLM-8b-v0.1-GGUF/blob/main/KOCDIGITAL-Kocdigital-LLM-8b-v0.1-Q4_K_S.gguf) | 4475.28 MB |
| Q5_K_M | [KOCDIGITAL-Kocdigital-LLM-8b-v0.1-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/KOCDIGITAL-Kocdigital-LLM-8b-v0.1-GGUF/blob/main/KOCDIGITAL-Kocdigital-LLM-8b-v0.1-Q5_K_M.gguf) | 5467.40 MB |
| Q5_K_S | [KOCDIGITAL-Kocdigital-LLM-8b-v0.1-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/KOCDIGITAL-Kocdigital-LLM-8b-v0.1-GGUF/blob/main/KOCDIGITAL-Kocdigital-LLM-8b-v0.1-Q5_K_S.gguf) | 5339.90 MB |
| Q6_K | [KOCDIGITAL-Kocdigital-LLM-8b-v0.1-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/KOCDIGITAL-Kocdigital-LLM-8b-v0.1-GGUF/blob/main/KOCDIGITAL-Kocdigital-LLM-8b-v0.1-Q6_K.gguf) | 6290.44 MB |
| Q8_0 | [KOCDIGITAL-Kocdigital-LLM-8b-v0.1-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/KOCDIGITAL-Kocdigital-LLM-8b-v0.1-GGUF/blob/main/KOCDIGITAL-Kocdigital-LLM-8b-v0.1-Q8_0.gguf) | 8145.11 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/maldv-badger-lambda-llama-3-8b-GGUF
|
featherless-ai-quants
| 2024-11-10T19:39:41Z | 31 | 0 | null |
[
"gguf",
"text-generation",
"base_model:maldv/badger-lambda-llama-3-8b",
"base_model:quantized:maldv/badger-lambda-llama-3-8b",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-05T10:03:02Z |
---
base_model: maldv/badger-lambda-llama-3-8b
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# maldv/badger-lambda-llama-3-8b GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [maldv-badger-lambda-llama-3-8b-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/maldv-badger-lambda-llama-3-8b-GGUF/blob/main/maldv-badger-lambda-llama-3-8b-IQ4_XS.gguf) | 4276.62 MB |
| Q2_K | [maldv-badger-lambda-llama-3-8b-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/maldv-badger-lambda-llama-3-8b-GGUF/blob/main/maldv-badger-lambda-llama-3-8b-Q2_K.gguf) | 3031.86 MB |
| Q3_K_L | [maldv-badger-lambda-llama-3-8b-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/maldv-badger-lambda-llama-3-8b-GGUF/blob/main/maldv-badger-lambda-llama-3-8b-Q3_K_L.gguf) | 4121.74 MB |
| Q3_K_M | [maldv-badger-lambda-llama-3-8b-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/maldv-badger-lambda-llama-3-8b-GGUF/blob/main/maldv-badger-lambda-llama-3-8b-Q3_K_M.gguf) | 3832.74 MB |
| Q3_K_S | [maldv-badger-lambda-llama-3-8b-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/maldv-badger-lambda-llama-3-8b-GGUF/blob/main/maldv-badger-lambda-llama-3-8b-Q3_K_S.gguf) | 3494.74 MB |
| Q4_K_M | [maldv-badger-lambda-llama-3-8b-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/maldv-badger-lambda-llama-3-8b-GGUF/blob/main/maldv-badger-lambda-llama-3-8b-Q4_K_M.gguf) | 4692.78 MB |
| Q4_K_S | [maldv-badger-lambda-llama-3-8b-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/maldv-badger-lambda-llama-3-8b-GGUF/blob/main/maldv-badger-lambda-llama-3-8b-Q4_K_S.gguf) | 4475.28 MB |
| Q5_K_M | [maldv-badger-lambda-llama-3-8b-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/maldv-badger-lambda-llama-3-8b-GGUF/blob/main/maldv-badger-lambda-llama-3-8b-Q5_K_M.gguf) | 5467.40 MB |
| Q5_K_S | [maldv-badger-lambda-llama-3-8b-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/maldv-badger-lambda-llama-3-8b-GGUF/blob/main/maldv-badger-lambda-llama-3-8b-Q5_K_S.gguf) | 5339.90 MB |
| Q6_K | [maldv-badger-lambda-llama-3-8b-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/maldv-badger-lambda-llama-3-8b-GGUF/blob/main/maldv-badger-lambda-llama-3-8b-Q6_K.gguf) | 6290.44 MB |
| Q8_0 | [maldv-badger-lambda-llama-3-8b-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/maldv-badger-lambda-llama-3-8b-GGUF/blob/main/maldv-badger-lambda-llama-3-8b-Q8_0.gguf) | 8145.11 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/NeverSleep-Lumimaid-v0.2-12B-GGUF
|
featherless-ai-quants
| 2024-11-10T19:39:38Z | 73 | 0 | null |
[
"gguf",
"text-generation",
"base_model:NeverSleep/Lumimaid-v0.2-12B",
"base_model:quantized:NeverSleep/Lumimaid-v0.2-12B",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-05T09:58:59Z |
---
base_model: NeverSleep/Lumimaid-v0.2-12B
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# NeverSleep/Lumimaid-v0.2-12B GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [NeverSleep-Lumimaid-v0.2-12B-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/NeverSleep-Lumimaid-v0.2-12B-GGUF/blob/main/NeverSleep-Lumimaid-v0.2-12B-IQ4_XS.gguf) | 6485.04 MB |
| Q2_K | [NeverSleep-Lumimaid-v0.2-12B-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/NeverSleep-Lumimaid-v0.2-12B-GGUF/blob/main/NeverSleep-Lumimaid-v0.2-12B-Q2_K.gguf) | 4569.10 MB |
| Q3_K_L | [NeverSleep-Lumimaid-v0.2-12B-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/NeverSleep-Lumimaid-v0.2-12B-GGUF/blob/main/NeverSleep-Lumimaid-v0.2-12B-Q3_K_L.gguf) | 6257.54 MB |
| Q3_K_M | [NeverSleep-Lumimaid-v0.2-12B-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/NeverSleep-Lumimaid-v0.2-12B-GGUF/blob/main/NeverSleep-Lumimaid-v0.2-12B-Q3_K_M.gguf) | 5801.29 MB |
| Q3_K_S | [NeverSleep-Lumimaid-v0.2-12B-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/NeverSleep-Lumimaid-v0.2-12B-GGUF/blob/main/NeverSleep-Lumimaid-v0.2-12B-Q3_K_S.gguf) | 5277.85 MB |
| Q4_K_M | [NeverSleep-Lumimaid-v0.2-12B-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/NeverSleep-Lumimaid-v0.2-12B-GGUF/blob/main/NeverSleep-Lumimaid-v0.2-12B-Q4_K_M.gguf) | 7130.82 MB |
| Q4_K_S | [NeverSleep-Lumimaid-v0.2-12B-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/NeverSleep-Lumimaid-v0.2-12B-GGUF/blob/main/NeverSleep-Lumimaid-v0.2-12B-Q4_K_S.gguf) | 6790.36 MB |
| Q5_K_M | [NeverSleep-Lumimaid-v0.2-12B-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/NeverSleep-Lumimaid-v0.2-12B-GGUF/blob/main/NeverSleep-Lumimaid-v0.2-12B-Q5_K_M.gguf) | 8323.32 MB |
| Q5_K_S | [NeverSleep-Lumimaid-v0.2-12B-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/NeverSleep-Lumimaid-v0.2-12B-GGUF/blob/main/NeverSleep-Lumimaid-v0.2-12B-Q5_K_S.gguf) | 8124.11 MB |
| Q6_K | [NeverSleep-Lumimaid-v0.2-12B-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/NeverSleep-Lumimaid-v0.2-12B-GGUF/blob/main/NeverSleep-Lumimaid-v0.2-12B-Q6_K.gguf) | 9590.36 MB |
| Q8_0 | [NeverSleep-Lumimaid-v0.2-12B-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/NeverSleep-Lumimaid-v0.2-12B-GGUF/blob/main/NeverSleep-Lumimaid-v0.2-12B-Q8_0.gguf) | 12419.11 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-GGUF
|
featherless-ai-quants
| 2024-11-10T19:39:27Z | 5 | 0 | null |
[
"gguf",
"text-generation",
"base_model:eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO",
"base_model:quantized:eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-05T08:55:36Z |
---
base_model: eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-GGUF/blob/main/eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-IQ4_XS.gguf) | 3761.66 MB |
| Q2_K | [eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-GGUF/blob/main/eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-Q2_K.gguf) | 2593.27 MB |
| Q3_K_L | [eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-GGUF/blob/main/eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-Q3_K_L.gguf) | 3644.97 MB |
| Q3_K_M | [eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-GGUF/blob/main/eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-Q3_K_M.gguf) | 3355.97 MB |
| Q3_K_S | [eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-GGUF/blob/main/eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-Q3_K_S.gguf) | 3017.97 MB |
| Q4_K_M | [eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-GGUF/blob/main/eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-Q4_K_M.gguf) | 4166.07 MB |
| Q4_K_S | [eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-GGUF/blob/main/eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-Q4_K_S.gguf) | 3948.57 MB |
| Q5_K_M | [eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-GGUF/blob/main/eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-Q5_K_M.gguf) | 4893.69 MB |
| Q5_K_S | [eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-GGUF/blob/main/eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-Q5_K_S.gguf) | 4766.19 MB |
| Q6_K | [eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-GGUF/blob/main/eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-Q6_K.gguf) | 5666.80 MB |
| Q8_0 | [eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-GGUF/blob/main/eren23-ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-Q8_0.gguf) | 7339.34 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/icefog72-WestIceLemonTeaRP-32k-7b-GGUF
|
featherless-ai-quants
| 2024-11-10T19:39:26Z | 16 | 0 | null |
[
"gguf",
"text-generation",
"base_model:icefog72/WestIceLemonTeaRP-32k-7b",
"base_model:quantized:icefog72/WestIceLemonTeaRP-32k-7b",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-05T08:55:17Z |
---
base_model: icefog72/WestIceLemonTeaRP-32k-7b
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# icefog72/WestIceLemonTeaRP-32k-7b GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [icefog72-WestIceLemonTeaRP-32k-7b-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/icefog72-WestIceLemonTeaRP-32k-7b-GGUF/blob/main/icefog72-WestIceLemonTeaRP-32k-7b-IQ4_XS.gguf) | 3761.66 MB |
| Q2_K | [icefog72-WestIceLemonTeaRP-32k-7b-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/icefog72-WestIceLemonTeaRP-32k-7b-GGUF/blob/main/icefog72-WestIceLemonTeaRP-32k-7b-Q2_K.gguf) | 2593.27 MB |
| Q3_K_L | [icefog72-WestIceLemonTeaRP-32k-7b-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/icefog72-WestIceLemonTeaRP-32k-7b-GGUF/blob/main/icefog72-WestIceLemonTeaRP-32k-7b-Q3_K_L.gguf) | 3644.97 MB |
| Q3_K_M | [icefog72-WestIceLemonTeaRP-32k-7b-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/icefog72-WestIceLemonTeaRP-32k-7b-GGUF/blob/main/icefog72-WestIceLemonTeaRP-32k-7b-Q3_K_M.gguf) | 3355.97 MB |
| Q3_K_S | [icefog72-WestIceLemonTeaRP-32k-7b-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/icefog72-WestIceLemonTeaRP-32k-7b-GGUF/blob/main/icefog72-WestIceLemonTeaRP-32k-7b-Q3_K_S.gguf) | 3017.97 MB |
| Q4_K_M | [icefog72-WestIceLemonTeaRP-32k-7b-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/icefog72-WestIceLemonTeaRP-32k-7b-GGUF/blob/main/icefog72-WestIceLemonTeaRP-32k-7b-Q4_K_M.gguf) | 4166.07 MB |
| Q4_K_S | [icefog72-WestIceLemonTeaRP-32k-7b-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/icefog72-WestIceLemonTeaRP-32k-7b-GGUF/blob/main/icefog72-WestIceLemonTeaRP-32k-7b-Q4_K_S.gguf) | 3948.57 MB |
| Q5_K_M | [icefog72-WestIceLemonTeaRP-32k-7b-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/icefog72-WestIceLemonTeaRP-32k-7b-GGUF/blob/main/icefog72-WestIceLemonTeaRP-32k-7b-Q5_K_M.gguf) | 4893.69 MB |
| Q5_K_S | [icefog72-WestIceLemonTeaRP-32k-7b-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/icefog72-WestIceLemonTeaRP-32k-7b-GGUF/blob/main/icefog72-WestIceLemonTeaRP-32k-7b-Q5_K_S.gguf) | 4766.19 MB |
| Q6_K | [icefog72-WestIceLemonTeaRP-32k-7b-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/icefog72-WestIceLemonTeaRP-32k-7b-GGUF/blob/main/icefog72-WestIceLemonTeaRP-32k-7b-Q6_K.gguf) | 5666.80 MB |
| Q8_0 | [icefog72-WestIceLemonTeaRP-32k-7b-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/icefog72-WestIceLemonTeaRP-32k-7b-GGUF/blob/main/icefog72-WestIceLemonTeaRP-32k-7b-Q8_0.gguf) | 7339.34 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/vicgalle-Configurable-Janus-7B-GGUF
|
featherless-ai-quants
| 2024-11-10T19:39:15Z | 59 | 0 | null |
[
"gguf",
"text-generation",
"base_model:vicgalle/Configurable-Janus-7B",
"base_model:quantized:vicgalle/Configurable-Janus-7B",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-05T08:24:09Z |
---
base_model: vicgalle/Configurable-Janus-7B
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# vicgalle/Configurable-Janus-7B GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [vicgalle-Configurable-Janus-7B-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/vicgalle-Configurable-Janus-7B-GGUF/blob/main/vicgalle-Configurable-Janus-7B-IQ4_XS.gguf) | 3761.66 MB |
| Q2_K | [vicgalle-Configurable-Janus-7B-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/vicgalle-Configurable-Janus-7B-GGUF/blob/main/vicgalle-Configurable-Janus-7B-Q2_K.gguf) | 2593.27 MB |
| Q3_K_L | [vicgalle-Configurable-Janus-7B-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/vicgalle-Configurable-Janus-7B-GGUF/blob/main/vicgalle-Configurable-Janus-7B-Q3_K_L.gguf) | 3644.97 MB |
| Q3_K_M | [vicgalle-Configurable-Janus-7B-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/vicgalle-Configurable-Janus-7B-GGUF/blob/main/vicgalle-Configurable-Janus-7B-Q3_K_M.gguf) | 3355.97 MB |
| Q3_K_S | [vicgalle-Configurable-Janus-7B-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/vicgalle-Configurable-Janus-7B-GGUF/blob/main/vicgalle-Configurable-Janus-7B-Q3_K_S.gguf) | 3017.97 MB |
| Q4_K_M | [vicgalle-Configurable-Janus-7B-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/vicgalle-Configurable-Janus-7B-GGUF/blob/main/vicgalle-Configurable-Janus-7B-Q4_K_M.gguf) | 4166.07 MB |
| Q4_K_S | [vicgalle-Configurable-Janus-7B-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/vicgalle-Configurable-Janus-7B-GGUF/blob/main/vicgalle-Configurable-Janus-7B-Q4_K_S.gguf) | 3948.57 MB |
| Q5_K_M | [vicgalle-Configurable-Janus-7B-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/vicgalle-Configurable-Janus-7B-GGUF/blob/main/vicgalle-Configurable-Janus-7B-Q5_K_M.gguf) | 4893.69 MB |
| Q5_K_S | [vicgalle-Configurable-Janus-7B-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/vicgalle-Configurable-Janus-7B-GGUF/blob/main/vicgalle-Configurable-Janus-7B-Q5_K_S.gguf) | 4766.19 MB |
| Q6_K | [vicgalle-Configurable-Janus-7B-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/vicgalle-Configurable-Janus-7B-GGUF/blob/main/vicgalle-Configurable-Janus-7B-Q6_K.gguf) | 5666.80 MB |
| Q8_0 | [vicgalle-Configurable-Janus-7B-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/vicgalle-Configurable-Janus-7B-GGUF/blob/main/vicgalle-Configurable-Janus-7B-Q8_0.gguf) | 7339.34 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/lcw99-llama-3-8b-it-ko-chang-GGUF
|
featherless-ai-quants
| 2024-11-10T19:39:08Z | 12 | 0 | null |
[
"gguf",
"text-generation",
"base_model:lcw99/llama-3-8b-it-ko-chang",
"base_model:quantized:lcw99/llama-3-8b-it-ko-chang",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-05T07:38:37Z |
---
base_model: lcw99/llama-3-8b-it-ko-chang
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# lcw99/llama-3-8b-it-ko-chang GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [lcw99-llama-3-8b-it-ko-chang-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/lcw99-llama-3-8b-it-ko-chang-GGUF/blob/main/lcw99-llama-3-8b-it-ko-chang-IQ4_XS.gguf) | 4276.62 MB |
| Q2_K | [lcw99-llama-3-8b-it-ko-chang-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/lcw99-llama-3-8b-it-ko-chang-GGUF/blob/main/lcw99-llama-3-8b-it-ko-chang-Q2_K.gguf) | 3031.86 MB |
| Q3_K_L | [lcw99-llama-3-8b-it-ko-chang-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/lcw99-llama-3-8b-it-ko-chang-GGUF/blob/main/lcw99-llama-3-8b-it-ko-chang-Q3_K_L.gguf) | 4121.74 MB |
| Q3_K_M | [lcw99-llama-3-8b-it-ko-chang-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/lcw99-llama-3-8b-it-ko-chang-GGUF/blob/main/lcw99-llama-3-8b-it-ko-chang-Q3_K_M.gguf) | 3832.74 MB |
| Q3_K_S | [lcw99-llama-3-8b-it-ko-chang-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/lcw99-llama-3-8b-it-ko-chang-GGUF/blob/main/lcw99-llama-3-8b-it-ko-chang-Q3_K_S.gguf) | 3494.74 MB |
| Q4_K_M | [lcw99-llama-3-8b-it-ko-chang-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/lcw99-llama-3-8b-it-ko-chang-GGUF/blob/main/lcw99-llama-3-8b-it-ko-chang-Q4_K_M.gguf) | 4692.78 MB |
| Q4_K_S | [lcw99-llama-3-8b-it-ko-chang-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/lcw99-llama-3-8b-it-ko-chang-GGUF/blob/main/lcw99-llama-3-8b-it-ko-chang-Q4_K_S.gguf) | 4475.28 MB |
| Q5_K_M | [lcw99-llama-3-8b-it-ko-chang-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/lcw99-llama-3-8b-it-ko-chang-GGUF/blob/main/lcw99-llama-3-8b-it-ko-chang-Q5_K_M.gguf) | 5467.40 MB |
| Q5_K_S | [lcw99-llama-3-8b-it-ko-chang-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/lcw99-llama-3-8b-it-ko-chang-GGUF/blob/main/lcw99-llama-3-8b-it-ko-chang-Q5_K_S.gguf) | 5339.90 MB |
| Q6_K | [lcw99-llama-3-8b-it-ko-chang-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/lcw99-llama-3-8b-it-ko-chang-GGUF/blob/main/lcw99-llama-3-8b-it-ko-chang-Q6_K.gguf) | 6290.44 MB |
| Q8_0 | [lcw99-llama-3-8b-it-ko-chang-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/lcw99-llama-3-8b-it-ko-chang-GGUF/blob/main/lcw99-llama-3-8b-it-ko-chang-Q8_0.gguf) | 8145.11 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/IntervitensInc-Mistral-Nemo-Base-2407-chatml-GGUF
|
featherless-ai-quants
| 2024-11-10T19:39:04Z | 14 | 0 | null |
[
"gguf",
"text-generation",
"base_model:IntervitensInc/Mistral-Nemo-Base-2407-chatml",
"base_model:quantized:IntervitensInc/Mistral-Nemo-Base-2407-chatml",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-05T07:30:26Z |
---
base_model: IntervitensInc/Mistral-Nemo-Base-2407-chatml
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# IntervitensInc/Mistral-Nemo-Base-2407-chatml GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [IntervitensInc-Mistral-Nemo-Base-2407-chatml-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/IntervitensInc-Mistral-Nemo-Base-2407-chatml-GGUF/blob/main/IntervitensInc-Mistral-Nemo-Base-2407-chatml-IQ4_XS.gguf) | 6485.04 MB |
| Q2_K | [IntervitensInc-Mistral-Nemo-Base-2407-chatml-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/IntervitensInc-Mistral-Nemo-Base-2407-chatml-GGUF/blob/main/IntervitensInc-Mistral-Nemo-Base-2407-chatml-Q2_K.gguf) | 4569.10 MB |
| Q3_K_L | [IntervitensInc-Mistral-Nemo-Base-2407-chatml-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/IntervitensInc-Mistral-Nemo-Base-2407-chatml-GGUF/blob/main/IntervitensInc-Mistral-Nemo-Base-2407-chatml-Q3_K_L.gguf) | 6257.54 MB |
| Q3_K_M | [IntervitensInc-Mistral-Nemo-Base-2407-chatml-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/IntervitensInc-Mistral-Nemo-Base-2407-chatml-GGUF/blob/main/IntervitensInc-Mistral-Nemo-Base-2407-chatml-Q3_K_M.gguf) | 5801.29 MB |
| Q3_K_S | [IntervitensInc-Mistral-Nemo-Base-2407-chatml-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/IntervitensInc-Mistral-Nemo-Base-2407-chatml-GGUF/blob/main/IntervitensInc-Mistral-Nemo-Base-2407-chatml-Q3_K_S.gguf) | 5277.85 MB |
| Q4_K_M | [IntervitensInc-Mistral-Nemo-Base-2407-chatml-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/IntervitensInc-Mistral-Nemo-Base-2407-chatml-GGUF/blob/main/IntervitensInc-Mistral-Nemo-Base-2407-chatml-Q4_K_M.gguf) | 7130.82 MB |
| Q4_K_S | [IntervitensInc-Mistral-Nemo-Base-2407-chatml-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/IntervitensInc-Mistral-Nemo-Base-2407-chatml-GGUF/blob/main/IntervitensInc-Mistral-Nemo-Base-2407-chatml-Q4_K_S.gguf) | 6790.35 MB |
| Q5_K_M | [IntervitensInc-Mistral-Nemo-Base-2407-chatml-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/IntervitensInc-Mistral-Nemo-Base-2407-chatml-GGUF/blob/main/IntervitensInc-Mistral-Nemo-Base-2407-chatml-Q5_K_M.gguf) | 8323.32 MB |
| Q5_K_S | [IntervitensInc-Mistral-Nemo-Base-2407-chatml-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/IntervitensInc-Mistral-Nemo-Base-2407-chatml-GGUF/blob/main/IntervitensInc-Mistral-Nemo-Base-2407-chatml-Q5_K_S.gguf) | 8124.10 MB |
| Q6_K | [IntervitensInc-Mistral-Nemo-Base-2407-chatml-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/IntervitensInc-Mistral-Nemo-Base-2407-chatml-GGUF/blob/main/IntervitensInc-Mistral-Nemo-Base-2407-chatml-Q6_K.gguf) | 9590.35 MB |
| Q8_0 | [IntervitensInc-Mistral-Nemo-Base-2407-chatml-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/IntervitensInc-Mistral-Nemo-Base-2407-chatml-GGUF/blob/main/IntervitensInc-Mistral-Nemo-Base-2407-chatml-Q8_0.gguf) | 12419.10 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/eren23-dpo-binarized-NeutrixOmnibe-7B-GGUF
|
featherless-ai-quants
| 2024-11-10T19:38:47Z | 17 | 0 | null |
[
"gguf",
"text-generation",
"base_model:eren23/dpo-binarized-NeutrixOmnibe-7B",
"base_model:quantized:eren23/dpo-binarized-NeutrixOmnibe-7B",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-05T05:53:13Z |
---
base_model: eren23/dpo-binarized-NeutrixOmnibe-7B
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# eren23/dpo-binarized-NeutrixOmnibe-7B GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [eren23-dpo-binarized-NeutrixOmnibe-7B-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/eren23-dpo-binarized-NeutrixOmnibe-7B-GGUF/blob/main/eren23-dpo-binarized-NeutrixOmnibe-7B-IQ4_XS.gguf) | 3761.66 MB |
| Q2_K | [eren23-dpo-binarized-NeutrixOmnibe-7B-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/eren23-dpo-binarized-NeutrixOmnibe-7B-GGUF/blob/main/eren23-dpo-binarized-NeutrixOmnibe-7B-Q2_K.gguf) | 2593.27 MB |
| Q3_K_L | [eren23-dpo-binarized-NeutrixOmnibe-7B-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/eren23-dpo-binarized-NeutrixOmnibe-7B-GGUF/blob/main/eren23-dpo-binarized-NeutrixOmnibe-7B-Q3_K_L.gguf) | 3644.97 MB |
| Q3_K_M | [eren23-dpo-binarized-NeutrixOmnibe-7B-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/eren23-dpo-binarized-NeutrixOmnibe-7B-GGUF/blob/main/eren23-dpo-binarized-NeutrixOmnibe-7B-Q3_K_M.gguf) | 3355.97 MB |
| Q3_K_S | [eren23-dpo-binarized-NeutrixOmnibe-7B-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/eren23-dpo-binarized-NeutrixOmnibe-7B-GGUF/blob/main/eren23-dpo-binarized-NeutrixOmnibe-7B-Q3_K_S.gguf) | 3017.97 MB |
| Q4_K_M | [eren23-dpo-binarized-NeutrixOmnibe-7B-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/eren23-dpo-binarized-NeutrixOmnibe-7B-GGUF/blob/main/eren23-dpo-binarized-NeutrixOmnibe-7B-Q4_K_M.gguf) | 4166.07 MB |
| Q4_K_S | [eren23-dpo-binarized-NeutrixOmnibe-7B-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/eren23-dpo-binarized-NeutrixOmnibe-7B-GGUF/blob/main/eren23-dpo-binarized-NeutrixOmnibe-7B-Q4_K_S.gguf) | 3948.57 MB |
| Q5_K_M | [eren23-dpo-binarized-NeutrixOmnibe-7B-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/eren23-dpo-binarized-NeutrixOmnibe-7B-GGUF/blob/main/eren23-dpo-binarized-NeutrixOmnibe-7B-Q5_K_M.gguf) | 4893.69 MB |
| Q5_K_S | [eren23-dpo-binarized-NeutrixOmnibe-7B-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/eren23-dpo-binarized-NeutrixOmnibe-7B-GGUF/blob/main/eren23-dpo-binarized-NeutrixOmnibe-7B-Q5_K_S.gguf) | 4766.19 MB |
| Q6_K | [eren23-dpo-binarized-NeutrixOmnibe-7B-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/eren23-dpo-binarized-NeutrixOmnibe-7B-GGUF/blob/main/eren23-dpo-binarized-NeutrixOmnibe-7B-Q6_K.gguf) | 5666.80 MB |
| Q8_0 | [eren23-dpo-binarized-NeutrixOmnibe-7B-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/eren23-dpo-binarized-NeutrixOmnibe-7B-GGUF/blob/main/eren23-dpo-binarized-NeutrixOmnibe-7B-Q8_0.gguf) | 7339.34 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/Azazelle-L3-RP_io-GGUF
|
featherless-ai-quants
| 2024-11-10T19:38:44Z | 8 | 0 | null |
[
"gguf",
"text-generation",
"base_model:Azazelle/L3-RP_io",
"base_model:quantized:Azazelle/L3-RP_io",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-05T05:40:48Z |
---
base_model: Azazelle/L3-RP_io
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# Azazelle/L3-RP_io GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [Azazelle-L3-RP_io-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/Azazelle-L3-RP_io-GGUF/blob/main/Azazelle-L3-RP_io-IQ4_XS.gguf) | 4276.62 MB |
| Q2_K | [Azazelle-L3-RP_io-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/Azazelle-L3-RP_io-GGUF/blob/main/Azazelle-L3-RP_io-Q2_K.gguf) | 3031.86 MB |
| Q3_K_L | [Azazelle-L3-RP_io-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/Azazelle-L3-RP_io-GGUF/blob/main/Azazelle-L3-RP_io-Q3_K_L.gguf) | 4121.74 MB |
| Q3_K_M | [Azazelle-L3-RP_io-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/Azazelle-L3-RP_io-GGUF/blob/main/Azazelle-L3-RP_io-Q3_K_M.gguf) | 3832.74 MB |
| Q3_K_S | [Azazelle-L3-RP_io-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/Azazelle-L3-RP_io-GGUF/blob/main/Azazelle-L3-RP_io-Q3_K_S.gguf) | 3494.74 MB |
| Q4_K_M | [Azazelle-L3-RP_io-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/Azazelle-L3-RP_io-GGUF/blob/main/Azazelle-L3-RP_io-Q4_K_M.gguf) | 4692.78 MB |
| Q4_K_S | [Azazelle-L3-RP_io-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/Azazelle-L3-RP_io-GGUF/blob/main/Azazelle-L3-RP_io-Q4_K_S.gguf) | 4475.28 MB |
| Q5_K_M | [Azazelle-L3-RP_io-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/Azazelle-L3-RP_io-GGUF/blob/main/Azazelle-L3-RP_io-Q5_K_M.gguf) | 5467.40 MB |
| Q5_K_S | [Azazelle-L3-RP_io-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/Azazelle-L3-RP_io-GGUF/blob/main/Azazelle-L3-RP_io-Q5_K_S.gguf) | 5339.90 MB |
| Q6_K | [Azazelle-L3-RP_io-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/Azazelle-L3-RP_io-GGUF/blob/main/Azazelle-L3-RP_io-Q6_K.gguf) | 6290.44 MB |
| Q8_0 | [Azazelle-L3-RP_io-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/Azazelle-L3-RP_io-GGUF/blob/main/Azazelle-L3-RP_io-Q8_0.gguf) | 8145.11 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/ohyeah1-Pantheon-Hermes-rp-GGUF
|
featherless-ai-quants
| 2024-11-10T19:38:41Z | 78 | 0 | null |
[
"gguf",
"text-generation",
"base_model:ohyeah1/Pantheon-Hermes-rp",
"base_model:quantized:ohyeah1/Pantheon-Hermes-rp",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-05T05:40:29Z |
---
base_model: ohyeah1/Pantheon-Hermes-rp
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# ohyeah1/Pantheon-Hermes-rp GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [ohyeah1-Pantheon-Hermes-rp-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/ohyeah1-Pantheon-Hermes-rp-GGUF/blob/main/ohyeah1-Pantheon-Hermes-rp-IQ4_XS.gguf) | 4276.62 MB |
| Q2_K | [ohyeah1-Pantheon-Hermes-rp-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/ohyeah1-Pantheon-Hermes-rp-GGUF/blob/main/ohyeah1-Pantheon-Hermes-rp-Q2_K.gguf) | 3031.86 MB |
| Q3_K_L | [ohyeah1-Pantheon-Hermes-rp-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/ohyeah1-Pantheon-Hermes-rp-GGUF/blob/main/ohyeah1-Pantheon-Hermes-rp-Q3_K_L.gguf) | 4121.74 MB |
| Q3_K_M | [ohyeah1-Pantheon-Hermes-rp-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/ohyeah1-Pantheon-Hermes-rp-GGUF/blob/main/ohyeah1-Pantheon-Hermes-rp-Q3_K_M.gguf) | 3832.74 MB |
| Q3_K_S | [ohyeah1-Pantheon-Hermes-rp-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/ohyeah1-Pantheon-Hermes-rp-GGUF/blob/main/ohyeah1-Pantheon-Hermes-rp-Q3_K_S.gguf) | 3494.74 MB |
| Q4_K_M | [ohyeah1-Pantheon-Hermes-rp-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/ohyeah1-Pantheon-Hermes-rp-GGUF/blob/main/ohyeah1-Pantheon-Hermes-rp-Q4_K_M.gguf) | 4692.78 MB |
| Q4_K_S | [ohyeah1-Pantheon-Hermes-rp-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/ohyeah1-Pantheon-Hermes-rp-GGUF/blob/main/ohyeah1-Pantheon-Hermes-rp-Q4_K_S.gguf) | 4475.28 MB |
| Q5_K_M | [ohyeah1-Pantheon-Hermes-rp-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/ohyeah1-Pantheon-Hermes-rp-GGUF/blob/main/ohyeah1-Pantheon-Hermes-rp-Q5_K_M.gguf) | 5467.40 MB |
| Q5_K_S | [ohyeah1-Pantheon-Hermes-rp-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/ohyeah1-Pantheon-Hermes-rp-GGUF/blob/main/ohyeah1-Pantheon-Hermes-rp-Q5_K_S.gguf) | 5339.90 MB |
| Q6_K | [ohyeah1-Pantheon-Hermes-rp-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/ohyeah1-Pantheon-Hermes-rp-GGUF/blob/main/ohyeah1-Pantheon-Hermes-rp-Q6_K.gguf) | 6290.44 MB |
| Q8_0 | [ohyeah1-Pantheon-Hermes-rp-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/ohyeah1-Pantheon-Hermes-rp-GGUF/blob/main/ohyeah1-Pantheon-Hermes-rp-Q8_0.gguf) | 8145.11 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/antiven0m-reverie-7b-GGUF
|
featherless-ai-quants
| 2024-11-10T19:38:39Z | 15 | 0 | null |
[
"gguf",
"text-generation",
"base_model:antiven0m/reverie-7b",
"base_model:quantized:antiven0m/reverie-7b",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-05T05:24:44Z |
---
base_model: antiven0m/reverie-7b
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# antiven0m/reverie-7b GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [antiven0m-reverie-7b-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/antiven0m-reverie-7b-GGUF/blob/main/antiven0m-reverie-7b-IQ4_XS.gguf) | 3761.66 MB |
| Q2_K | [antiven0m-reverie-7b-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/antiven0m-reverie-7b-GGUF/blob/main/antiven0m-reverie-7b-Q2_K.gguf) | 2593.27 MB |
| Q3_K_L | [antiven0m-reverie-7b-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/antiven0m-reverie-7b-GGUF/blob/main/antiven0m-reverie-7b-Q3_K_L.gguf) | 3644.97 MB |
| Q3_K_M | [antiven0m-reverie-7b-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/antiven0m-reverie-7b-GGUF/blob/main/antiven0m-reverie-7b-Q3_K_M.gguf) | 3355.97 MB |
| Q3_K_S | [antiven0m-reverie-7b-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/antiven0m-reverie-7b-GGUF/blob/main/antiven0m-reverie-7b-Q3_K_S.gguf) | 3017.97 MB |
| Q4_K_M | [antiven0m-reverie-7b-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/antiven0m-reverie-7b-GGUF/blob/main/antiven0m-reverie-7b-Q4_K_M.gguf) | 4166.07 MB |
| Q4_K_S | [antiven0m-reverie-7b-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/antiven0m-reverie-7b-GGUF/blob/main/antiven0m-reverie-7b-Q4_K_S.gguf) | 3948.57 MB |
| Q5_K_M | [antiven0m-reverie-7b-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/antiven0m-reverie-7b-GGUF/blob/main/antiven0m-reverie-7b-Q5_K_M.gguf) | 4893.70 MB |
| Q5_K_S | [antiven0m-reverie-7b-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/antiven0m-reverie-7b-GGUF/blob/main/antiven0m-reverie-7b-Q5_K_S.gguf) | 4766.20 MB |
| Q6_K | [antiven0m-reverie-7b-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/antiven0m-reverie-7b-GGUF/blob/main/antiven0m-reverie-7b-Q6_K.gguf) | 5666.80 MB |
| Q8_0 | [antiven0m-reverie-7b-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/antiven0m-reverie-7b-GGUF/blob/main/antiven0m-reverie-7b-Q8_0.gguf) | 7339.34 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/jdqqjr-llama3-8b-instruct-uncensored-JR-GGUF
|
featherless-ai-quants
| 2024-11-10T19:38:29Z | 12 | 0 | null |
[
"gguf",
"text-generation",
"base_model:jdqqjr/llama3-8b-instruct-uncensored-JR",
"base_model:quantized:jdqqjr/llama3-8b-instruct-uncensored-JR",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-05T04:53:14Z |
---
base_model: jdqqjr/llama3-8b-instruct-uncensored-JR
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# jdqqjr/llama3-8b-instruct-uncensored-JR GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [jdqqjr-llama3-8b-instruct-uncensored-JR-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/jdqqjr-llama3-8b-instruct-uncensored-JR-GGUF/blob/main/jdqqjr-llama3-8b-instruct-uncensored-JR-IQ4_XS.gguf) | 4276.62 MB |
| Q2_K | [jdqqjr-llama3-8b-instruct-uncensored-JR-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/jdqqjr-llama3-8b-instruct-uncensored-JR-GGUF/blob/main/jdqqjr-llama3-8b-instruct-uncensored-JR-Q2_K.gguf) | 3031.86 MB |
| Q3_K_L | [jdqqjr-llama3-8b-instruct-uncensored-JR-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/jdqqjr-llama3-8b-instruct-uncensored-JR-GGUF/blob/main/jdqqjr-llama3-8b-instruct-uncensored-JR-Q3_K_L.gguf) | 4121.74 MB |
| Q3_K_M | [jdqqjr-llama3-8b-instruct-uncensored-JR-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/jdqqjr-llama3-8b-instruct-uncensored-JR-GGUF/blob/main/jdqqjr-llama3-8b-instruct-uncensored-JR-Q3_K_M.gguf) | 3832.74 MB |
| Q3_K_S | [jdqqjr-llama3-8b-instruct-uncensored-JR-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/jdqqjr-llama3-8b-instruct-uncensored-JR-GGUF/blob/main/jdqqjr-llama3-8b-instruct-uncensored-JR-Q3_K_S.gguf) | 3494.74 MB |
| Q4_K_M | [jdqqjr-llama3-8b-instruct-uncensored-JR-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/jdqqjr-llama3-8b-instruct-uncensored-JR-GGUF/blob/main/jdqqjr-llama3-8b-instruct-uncensored-JR-Q4_K_M.gguf) | 4692.78 MB |
| Q4_K_S | [jdqqjr-llama3-8b-instruct-uncensored-JR-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/jdqqjr-llama3-8b-instruct-uncensored-JR-GGUF/blob/main/jdqqjr-llama3-8b-instruct-uncensored-JR-Q4_K_S.gguf) | 4475.28 MB |
| Q5_K_M | [jdqqjr-llama3-8b-instruct-uncensored-JR-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/jdqqjr-llama3-8b-instruct-uncensored-JR-GGUF/blob/main/jdqqjr-llama3-8b-instruct-uncensored-JR-Q5_K_M.gguf) | 5467.40 MB |
| Q5_K_S | [jdqqjr-llama3-8b-instruct-uncensored-JR-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/jdqqjr-llama3-8b-instruct-uncensored-JR-GGUF/blob/main/jdqqjr-llama3-8b-instruct-uncensored-JR-Q5_K_S.gguf) | 5339.90 MB |
| Q6_K | [jdqqjr-llama3-8b-instruct-uncensored-JR-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/jdqqjr-llama3-8b-instruct-uncensored-JR-GGUF/blob/main/jdqqjr-llama3-8b-instruct-uncensored-JR-Q6_K.gguf) | 6290.44 MB |
| Q8_0 | [jdqqjr-llama3-8b-instruct-uncensored-JR-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/jdqqjr-llama3-8b-instruct-uncensored-JR-GGUF/blob/main/jdqqjr-llama3-8b-instruct-uncensored-JR-Q8_0.gguf) | 8145.11 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/Luni-StarDust-12b-v2-GGUF
|
featherless-ai-quants
| 2024-11-10T19:38:22Z | 24 | 0 | null |
[
"gguf",
"text-generation",
"base_model:Luni/StarDust-12b-v2",
"base_model:quantized:Luni/StarDust-12b-v2",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-05T04:39:26Z |
---
base_model: Luni/StarDust-12b-v2
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# Luni/StarDust-12b-v2 GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [Luni-StarDust-12b-v2-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/Luni-StarDust-12b-v2-GGUF/blob/main/Luni-StarDust-12b-v2-IQ4_XS.gguf) | 6485.04 MB |
| Q2_K | [Luni-StarDust-12b-v2-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/Luni-StarDust-12b-v2-GGUF/blob/main/Luni-StarDust-12b-v2-Q2_K.gguf) | 4569.10 MB |
| Q3_K_L | [Luni-StarDust-12b-v2-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/Luni-StarDust-12b-v2-GGUF/blob/main/Luni-StarDust-12b-v2-Q3_K_L.gguf) | 6257.54 MB |
| Q3_K_M | [Luni-StarDust-12b-v2-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/Luni-StarDust-12b-v2-GGUF/blob/main/Luni-StarDust-12b-v2-Q3_K_M.gguf) | 5801.29 MB |
| Q3_K_S | [Luni-StarDust-12b-v2-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/Luni-StarDust-12b-v2-GGUF/blob/main/Luni-StarDust-12b-v2-Q3_K_S.gguf) | 5277.85 MB |
| Q4_K_M | [Luni-StarDust-12b-v2-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/Luni-StarDust-12b-v2-GGUF/blob/main/Luni-StarDust-12b-v2-Q4_K_M.gguf) | 7130.82 MB |
| Q4_K_S | [Luni-StarDust-12b-v2-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/Luni-StarDust-12b-v2-GGUF/blob/main/Luni-StarDust-12b-v2-Q4_K_S.gguf) | 6790.35 MB |
| Q5_K_M | [Luni-StarDust-12b-v2-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/Luni-StarDust-12b-v2-GGUF/blob/main/Luni-StarDust-12b-v2-Q5_K_M.gguf) | 8323.32 MB |
| Q5_K_S | [Luni-StarDust-12b-v2-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/Luni-StarDust-12b-v2-GGUF/blob/main/Luni-StarDust-12b-v2-Q5_K_S.gguf) | 8124.10 MB |
| Q6_K | [Luni-StarDust-12b-v2-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/Luni-StarDust-12b-v2-GGUF/blob/main/Luni-StarDust-12b-v2-Q6_K.gguf) | 9590.35 MB |
| Q8_0 | [Luni-StarDust-12b-v2-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/Luni-StarDust-12b-v2-GGUF/blob/main/Luni-StarDust-12b-v2-Q8_0.gguf) | 12419.10 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-GGUF
|
featherless-ai-quants
| 2024-11-10T19:38:15Z | 14 | 0 | null |
[
"gguf",
"text-generation",
"base_model:BarryFutureman/WestLakeX-7B-EvoMerge-Variant2",
"base_model:quantized:BarryFutureman/WestLakeX-7B-EvoMerge-Variant2",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-05T04:18:09Z |
---
base_model: BarryFutureman/WestLakeX-7B-EvoMerge-Variant2
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# BarryFutureman/WestLakeX-7B-EvoMerge-Variant2 GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-GGUF/blob/main/BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-IQ4_XS.gguf) | 3761.66 MB |
| Q2_K | [BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-GGUF/blob/main/BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-Q2_K.gguf) | 2593.27 MB |
| Q3_K_L | [BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-GGUF/blob/main/BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-Q3_K_L.gguf) | 3644.97 MB |
| Q3_K_M | [BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-GGUF/blob/main/BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-Q3_K_M.gguf) | 3355.97 MB |
| Q3_K_S | [BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-GGUF/blob/main/BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-Q3_K_S.gguf) | 3017.97 MB |
| Q4_K_M | [BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-GGUF/blob/main/BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-Q4_K_M.gguf) | 4166.07 MB |
| Q4_K_S | [BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-GGUF/blob/main/BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-Q4_K_S.gguf) | 3948.57 MB |
| Q5_K_M | [BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-GGUF/blob/main/BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-Q5_K_M.gguf) | 4893.69 MB |
| Q5_K_S | [BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-GGUF/blob/main/BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-Q5_K_S.gguf) | 4766.19 MB |
| Q6_K | [BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-GGUF/blob/main/BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-Q6_K.gguf) | 5666.80 MB |
| Q8_0 | [BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-GGUF/blob/main/BarryFutureman-WestLakeX-7B-EvoMerge-Variant2-Q8_0.gguf) | 7339.34 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/uukuguy-speechless-mistral-hermes-code-7b-GGUF
|
featherless-ai-quants
| 2024-11-10T19:38:11Z | 29 | 0 | null |
[
"gguf",
"text-generation",
"base_model:uukuguy/speechless-mistral-hermes-code-7b",
"base_model:quantized:uukuguy/speechless-mistral-hermes-code-7b",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-05T04:15:02Z |
---
base_model: uukuguy/speechless-mistral-hermes-code-7b
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# uukuguy/speechless-mistral-hermes-code-7b GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [uukuguy-speechless-mistral-hermes-code-7b-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/uukuguy-speechless-mistral-hermes-code-7b-GGUF/blob/main/uukuguy-speechless-mistral-hermes-code-7b-IQ4_XS.gguf) | 3761.66 MB |
| Q2_K | [uukuguy-speechless-mistral-hermes-code-7b-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/uukuguy-speechless-mistral-hermes-code-7b-GGUF/blob/main/uukuguy-speechless-mistral-hermes-code-7b-Q2_K.gguf) | 2593.27 MB |
| Q3_K_L | [uukuguy-speechless-mistral-hermes-code-7b-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/uukuguy-speechless-mistral-hermes-code-7b-GGUF/blob/main/uukuguy-speechless-mistral-hermes-code-7b-Q3_K_L.gguf) | 3644.97 MB |
| Q3_K_M | [uukuguy-speechless-mistral-hermes-code-7b-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/uukuguy-speechless-mistral-hermes-code-7b-GGUF/blob/main/uukuguy-speechless-mistral-hermes-code-7b-Q3_K_M.gguf) | 3355.97 MB |
| Q3_K_S | [uukuguy-speechless-mistral-hermes-code-7b-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/uukuguy-speechless-mistral-hermes-code-7b-GGUF/blob/main/uukuguy-speechless-mistral-hermes-code-7b-Q3_K_S.gguf) | 3017.97 MB |
| Q4_K_M | [uukuguy-speechless-mistral-hermes-code-7b-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/uukuguy-speechless-mistral-hermes-code-7b-GGUF/blob/main/uukuguy-speechless-mistral-hermes-code-7b-Q4_K_M.gguf) | 4166.07 MB |
| Q4_K_S | [uukuguy-speechless-mistral-hermes-code-7b-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/uukuguy-speechless-mistral-hermes-code-7b-GGUF/blob/main/uukuguy-speechless-mistral-hermes-code-7b-Q4_K_S.gguf) | 3948.57 MB |
| Q5_K_M | [uukuguy-speechless-mistral-hermes-code-7b-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/uukuguy-speechless-mistral-hermes-code-7b-GGUF/blob/main/uukuguy-speechless-mistral-hermes-code-7b-Q5_K_M.gguf) | 4893.69 MB |
| Q5_K_S | [uukuguy-speechless-mistral-hermes-code-7b-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/uukuguy-speechless-mistral-hermes-code-7b-GGUF/blob/main/uukuguy-speechless-mistral-hermes-code-7b-Q5_K_S.gguf) | 4766.19 MB |
| Q6_K | [uukuguy-speechless-mistral-hermes-code-7b-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/uukuguy-speechless-mistral-hermes-code-7b-GGUF/blob/main/uukuguy-speechless-mistral-hermes-code-7b-Q6_K.gguf) | 5666.80 MB |
| Q8_0 | [uukuguy-speechless-mistral-hermes-code-7b-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/uukuguy-speechless-mistral-hermes-code-7b-GGUF/blob/main/uukuguy-speechless-mistral-hermes-code-7b-Q8_0.gguf) | 7339.34 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/Kukedlc-NeuTrixOmniBe-DPO-GGUF
|
featherless-ai-quants
| 2024-11-10T19:38:04Z | 12 | 0 | null |
[
"gguf",
"text-generation",
"base_model:Kukedlc/NeuTrixOmniBe-DPO",
"base_model:quantized:Kukedlc/NeuTrixOmniBe-DPO",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-05T03:51:28Z |
---
base_model: Kukedlc/NeuTrixOmniBe-DPO
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# Kukedlc/NeuTrixOmniBe-DPO GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [Kukedlc-NeuTrixOmniBe-DPO-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/Kukedlc-NeuTrixOmniBe-DPO-GGUF/blob/main/Kukedlc-NeuTrixOmniBe-DPO-IQ4_XS.gguf) | 3761.66 MB |
| Q2_K | [Kukedlc-NeuTrixOmniBe-DPO-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/Kukedlc-NeuTrixOmniBe-DPO-GGUF/blob/main/Kukedlc-NeuTrixOmniBe-DPO-Q2_K.gguf) | 2593.27 MB |
| Q3_K_L | [Kukedlc-NeuTrixOmniBe-DPO-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/Kukedlc-NeuTrixOmniBe-DPO-GGUF/blob/main/Kukedlc-NeuTrixOmniBe-DPO-Q3_K_L.gguf) | 3644.97 MB |
| Q3_K_M | [Kukedlc-NeuTrixOmniBe-DPO-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/Kukedlc-NeuTrixOmniBe-DPO-GGUF/blob/main/Kukedlc-NeuTrixOmniBe-DPO-Q3_K_M.gguf) | 3355.97 MB |
| Q3_K_S | [Kukedlc-NeuTrixOmniBe-DPO-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/Kukedlc-NeuTrixOmniBe-DPO-GGUF/blob/main/Kukedlc-NeuTrixOmniBe-DPO-Q3_K_S.gguf) | 3017.97 MB |
| Q4_K_M | [Kukedlc-NeuTrixOmniBe-DPO-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/Kukedlc-NeuTrixOmniBe-DPO-GGUF/blob/main/Kukedlc-NeuTrixOmniBe-DPO-Q4_K_M.gguf) | 4166.07 MB |
| Q4_K_S | [Kukedlc-NeuTrixOmniBe-DPO-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/Kukedlc-NeuTrixOmniBe-DPO-GGUF/blob/main/Kukedlc-NeuTrixOmniBe-DPO-Q4_K_S.gguf) | 3948.57 MB |
| Q5_K_M | [Kukedlc-NeuTrixOmniBe-DPO-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/Kukedlc-NeuTrixOmniBe-DPO-GGUF/blob/main/Kukedlc-NeuTrixOmniBe-DPO-Q5_K_M.gguf) | 4893.69 MB |
| Q5_K_S | [Kukedlc-NeuTrixOmniBe-DPO-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/Kukedlc-NeuTrixOmniBe-DPO-GGUF/blob/main/Kukedlc-NeuTrixOmniBe-DPO-Q5_K_S.gguf) | 4766.19 MB |
| Q6_K | [Kukedlc-NeuTrixOmniBe-DPO-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/Kukedlc-NeuTrixOmniBe-DPO-GGUF/blob/main/Kukedlc-NeuTrixOmniBe-DPO-Q6_K.gguf) | 5666.80 MB |
| Q8_0 | [Kukedlc-NeuTrixOmniBe-DPO-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/Kukedlc-NeuTrixOmniBe-DPO-GGUF/blob/main/Kukedlc-NeuTrixOmniBe-DPO-Q8_0.gguf) | 7339.34 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/Gryphe-Pantheon-RP-1.6-12b-Nemo-GGUF
|
featherless-ai-quants
| 2024-11-10T19:37:58Z | 13 | 0 | null |
[
"gguf",
"text-generation",
"base_model:Gryphe/Pantheon-RP-1.6-12b-Nemo",
"base_model:quantized:Gryphe/Pantheon-RP-1.6-12b-Nemo",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-05T03:36:29Z |
---
base_model: Gryphe/Pantheon-RP-1.6-12b-Nemo
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# Gryphe/Pantheon-RP-1.6-12b-Nemo GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [Gryphe-Pantheon-RP-1.6-12b-Nemo-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/Gryphe-Pantheon-RP-1.6-12b-Nemo-GGUF/blob/main/Gryphe-Pantheon-RP-1.6-12b-Nemo-IQ4_XS.gguf) | 6485.04 MB |
| Q2_K | [Gryphe-Pantheon-RP-1.6-12b-Nemo-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/Gryphe-Pantheon-RP-1.6-12b-Nemo-GGUF/blob/main/Gryphe-Pantheon-RP-1.6-12b-Nemo-Q2_K.gguf) | 4569.10 MB |
| Q3_K_L | [Gryphe-Pantheon-RP-1.6-12b-Nemo-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/Gryphe-Pantheon-RP-1.6-12b-Nemo-GGUF/blob/main/Gryphe-Pantheon-RP-1.6-12b-Nemo-Q3_K_L.gguf) | 6257.54 MB |
| Q3_K_M | [Gryphe-Pantheon-RP-1.6-12b-Nemo-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/Gryphe-Pantheon-RP-1.6-12b-Nemo-GGUF/blob/main/Gryphe-Pantheon-RP-1.6-12b-Nemo-Q3_K_M.gguf) | 5801.29 MB |
| Q3_K_S | [Gryphe-Pantheon-RP-1.6-12b-Nemo-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/Gryphe-Pantheon-RP-1.6-12b-Nemo-GGUF/blob/main/Gryphe-Pantheon-RP-1.6-12b-Nemo-Q3_K_S.gguf) | 5277.85 MB |
| Q4_K_M | [Gryphe-Pantheon-RP-1.6-12b-Nemo-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/Gryphe-Pantheon-RP-1.6-12b-Nemo-GGUF/blob/main/Gryphe-Pantheon-RP-1.6-12b-Nemo-Q4_K_M.gguf) | 7130.82 MB |
| Q4_K_S | [Gryphe-Pantheon-RP-1.6-12b-Nemo-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/Gryphe-Pantheon-RP-1.6-12b-Nemo-GGUF/blob/main/Gryphe-Pantheon-RP-1.6-12b-Nemo-Q4_K_S.gguf) | 6790.35 MB |
| Q5_K_M | [Gryphe-Pantheon-RP-1.6-12b-Nemo-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/Gryphe-Pantheon-RP-1.6-12b-Nemo-GGUF/blob/main/Gryphe-Pantheon-RP-1.6-12b-Nemo-Q5_K_M.gguf) | 8323.32 MB |
| Q5_K_S | [Gryphe-Pantheon-RP-1.6-12b-Nemo-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/Gryphe-Pantheon-RP-1.6-12b-Nemo-GGUF/blob/main/Gryphe-Pantheon-RP-1.6-12b-Nemo-Q5_K_S.gguf) | 8124.10 MB |
| Q6_K | [Gryphe-Pantheon-RP-1.6-12b-Nemo-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/Gryphe-Pantheon-RP-1.6-12b-Nemo-GGUF/blob/main/Gryphe-Pantheon-RP-1.6-12b-Nemo-Q6_K.gguf) | 9590.35 MB |
| Q8_0 | [Gryphe-Pantheon-RP-1.6-12b-Nemo-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/Gryphe-Pantheon-RP-1.6-12b-Nemo-GGUF/blob/main/Gryphe-Pantheon-RP-1.6-12b-Nemo-Q8_0.gguf) | 12419.10 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/NurtureAI-Meta-Llama-3-8B-Instruct-64k-GGUF
|
featherless-ai-quants
| 2024-11-10T19:37:45Z | 15 | 0 | null |
[
"gguf",
"text-generation",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-05T02:38:55Z |
---
base_model: NurtureAI/Meta-Llama-3-8B-Instruct-64k
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# NurtureAI/Meta-Llama-3-8B-Instruct-64k GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [NurtureAI-Meta-Llama-3-8B-Instruct-64k-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/NurtureAI-Meta-Llama-3-8B-Instruct-64k-GGUF/blob/main/NurtureAI-Meta-Llama-3-8B-Instruct-64k-IQ4_XS.gguf) | 4276.62 MB |
| Q2_K | [NurtureAI-Meta-Llama-3-8B-Instruct-64k-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/NurtureAI-Meta-Llama-3-8B-Instruct-64k-GGUF/blob/main/NurtureAI-Meta-Llama-3-8B-Instruct-64k-Q2_K.gguf) | 3031.86 MB |
| Q3_K_L | [NurtureAI-Meta-Llama-3-8B-Instruct-64k-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/NurtureAI-Meta-Llama-3-8B-Instruct-64k-GGUF/blob/main/NurtureAI-Meta-Llama-3-8B-Instruct-64k-Q3_K_L.gguf) | 4121.74 MB |
| Q3_K_M | [NurtureAI-Meta-Llama-3-8B-Instruct-64k-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/NurtureAI-Meta-Llama-3-8B-Instruct-64k-GGUF/blob/main/NurtureAI-Meta-Llama-3-8B-Instruct-64k-Q3_K_M.gguf) | 3832.74 MB |
| Q3_K_S | [NurtureAI-Meta-Llama-3-8B-Instruct-64k-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/NurtureAI-Meta-Llama-3-8B-Instruct-64k-GGUF/blob/main/NurtureAI-Meta-Llama-3-8B-Instruct-64k-Q3_K_S.gguf) | 3494.74 MB |
| Q4_K_M | [NurtureAI-Meta-Llama-3-8B-Instruct-64k-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/NurtureAI-Meta-Llama-3-8B-Instruct-64k-GGUF/blob/main/NurtureAI-Meta-Llama-3-8B-Instruct-64k-Q4_K_M.gguf) | 4692.78 MB |
| Q4_K_S | [NurtureAI-Meta-Llama-3-8B-Instruct-64k-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/NurtureAI-Meta-Llama-3-8B-Instruct-64k-GGUF/blob/main/NurtureAI-Meta-Llama-3-8B-Instruct-64k-Q4_K_S.gguf) | 4475.28 MB |
| Q5_K_M | [NurtureAI-Meta-Llama-3-8B-Instruct-64k-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/NurtureAI-Meta-Llama-3-8B-Instruct-64k-GGUF/blob/main/NurtureAI-Meta-Llama-3-8B-Instruct-64k-Q5_K_M.gguf) | 5467.40 MB |
| Q5_K_S | [NurtureAI-Meta-Llama-3-8B-Instruct-64k-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/NurtureAI-Meta-Llama-3-8B-Instruct-64k-GGUF/blob/main/NurtureAI-Meta-Llama-3-8B-Instruct-64k-Q5_K_S.gguf) | 5339.90 MB |
| Q6_K | [NurtureAI-Meta-Llama-3-8B-Instruct-64k-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/NurtureAI-Meta-Llama-3-8B-Instruct-64k-GGUF/blob/main/NurtureAI-Meta-Llama-3-8B-Instruct-64k-Q6_K.gguf) | 6290.44 MB |
| Q8_0 | [NurtureAI-Meta-Llama-3-8B-Instruct-64k-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/NurtureAI-Meta-Llama-3-8B-Instruct-64k-GGUF/blob/main/NurtureAI-Meta-Llama-3-8B-Instruct-64k-Q8_0.gguf) | 8145.11 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/bongbongs-NewMes-v15-GGUF
|
featherless-ai-quants
| 2024-11-10T19:37:39Z | 15 | 0 | null |
[
"gguf",
"text-generation",
"base_model:bongbongs/NewMes-v15",
"base_model:quantized:bongbongs/NewMes-v15",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-05T02:30:48Z |
---
base_model: bongbongs/NewMes-v15
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# bongbongs/NewMes-v15 GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [bongbongs-NewMes-v15-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/bongbongs-NewMes-v15-GGUF/blob/main/bongbongs-NewMes-v15-IQ4_XS.gguf) | 4276.62 MB |
| Q2_K | [bongbongs-NewMes-v15-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/bongbongs-NewMes-v15-GGUF/blob/main/bongbongs-NewMes-v15-Q2_K.gguf) | 3031.86 MB |
| Q3_K_L | [bongbongs-NewMes-v15-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/bongbongs-NewMes-v15-GGUF/blob/main/bongbongs-NewMes-v15-Q3_K_L.gguf) | 4121.74 MB |
| Q3_K_M | [bongbongs-NewMes-v15-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/bongbongs-NewMes-v15-GGUF/blob/main/bongbongs-NewMes-v15-Q3_K_M.gguf) | 3832.74 MB |
| Q3_K_S | [bongbongs-NewMes-v15-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/bongbongs-NewMes-v15-GGUF/blob/main/bongbongs-NewMes-v15-Q3_K_S.gguf) | 3494.74 MB |
| Q4_K_M | [bongbongs-NewMes-v15-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/bongbongs-NewMes-v15-GGUF/blob/main/bongbongs-NewMes-v15-Q4_K_M.gguf) | 4692.78 MB |
| Q4_K_S | [bongbongs-NewMes-v15-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/bongbongs-NewMes-v15-GGUF/blob/main/bongbongs-NewMes-v15-Q4_K_S.gguf) | 4475.28 MB |
| Q5_K_M | [bongbongs-NewMes-v15-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/bongbongs-NewMes-v15-GGUF/blob/main/bongbongs-NewMes-v15-Q5_K_M.gguf) | 5467.40 MB |
| Q5_K_S | [bongbongs-NewMes-v15-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/bongbongs-NewMes-v15-GGUF/blob/main/bongbongs-NewMes-v15-Q5_K_S.gguf) | 5339.90 MB |
| Q6_K | [bongbongs-NewMes-v15-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/bongbongs-NewMes-v15-GGUF/blob/main/bongbongs-NewMes-v15-Q6_K.gguf) | 6290.44 MB |
| Q8_0 | [bongbongs-NewMes-v15-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/bongbongs-NewMes-v15-GGUF/blob/main/bongbongs-NewMes-v15-Q8_0.gguf) | 8145.11 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/MaziyarPanahi-Llama-3-8B-Instruct-v0.9-GGUF
|
featherless-ai-quants
| 2024-11-10T19:37:14Z | 8 | 0 | null |
[
"gguf",
"text-generation",
"base_model:MaziyarPanahi/Llama-3-8B-Instruct-v0.9",
"base_model:quantized:MaziyarPanahi/Llama-3-8B-Instruct-v0.9",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-05T01:11:45Z |
---
base_model: MaziyarPanahi/Llama-3-8B-Instruct-v0.9
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# MaziyarPanahi/Llama-3-8B-Instruct-v0.9 GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [MaziyarPanahi-Llama-3-8B-Instruct-v0.9-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/MaziyarPanahi-Llama-3-8B-Instruct-v0.9-GGUF/blob/main/MaziyarPanahi-Llama-3-8B-Instruct-v0.9-IQ4_XS.gguf) | 4276.62 MB |
| Q2_K | [MaziyarPanahi-Llama-3-8B-Instruct-v0.9-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/MaziyarPanahi-Llama-3-8B-Instruct-v0.9-GGUF/blob/main/MaziyarPanahi-Llama-3-8B-Instruct-v0.9-Q2_K.gguf) | 3031.86 MB |
| Q3_K_L | [MaziyarPanahi-Llama-3-8B-Instruct-v0.9-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/MaziyarPanahi-Llama-3-8B-Instruct-v0.9-GGUF/blob/main/MaziyarPanahi-Llama-3-8B-Instruct-v0.9-Q3_K_L.gguf) | 4121.74 MB |
| Q3_K_M | [MaziyarPanahi-Llama-3-8B-Instruct-v0.9-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/MaziyarPanahi-Llama-3-8B-Instruct-v0.9-GGUF/blob/main/MaziyarPanahi-Llama-3-8B-Instruct-v0.9-Q3_K_M.gguf) | 3832.74 MB |
| Q3_K_S | [MaziyarPanahi-Llama-3-8B-Instruct-v0.9-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/MaziyarPanahi-Llama-3-8B-Instruct-v0.9-GGUF/blob/main/MaziyarPanahi-Llama-3-8B-Instruct-v0.9-Q3_K_S.gguf) | 3494.74 MB |
| Q4_K_M | [MaziyarPanahi-Llama-3-8B-Instruct-v0.9-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/MaziyarPanahi-Llama-3-8B-Instruct-v0.9-GGUF/blob/main/MaziyarPanahi-Llama-3-8B-Instruct-v0.9-Q4_K_M.gguf) | 4692.78 MB |
| Q4_K_S | [MaziyarPanahi-Llama-3-8B-Instruct-v0.9-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/MaziyarPanahi-Llama-3-8B-Instruct-v0.9-GGUF/blob/main/MaziyarPanahi-Llama-3-8B-Instruct-v0.9-Q4_K_S.gguf) | 4475.28 MB |
| Q5_K_M | [MaziyarPanahi-Llama-3-8B-Instruct-v0.9-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/MaziyarPanahi-Llama-3-8B-Instruct-v0.9-GGUF/blob/main/MaziyarPanahi-Llama-3-8B-Instruct-v0.9-Q5_K_M.gguf) | 5467.40 MB |
| Q5_K_S | [MaziyarPanahi-Llama-3-8B-Instruct-v0.9-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/MaziyarPanahi-Llama-3-8B-Instruct-v0.9-GGUF/blob/main/MaziyarPanahi-Llama-3-8B-Instruct-v0.9-Q5_K_S.gguf) | 5339.90 MB |
| Q6_K | [MaziyarPanahi-Llama-3-8B-Instruct-v0.9-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/MaziyarPanahi-Llama-3-8B-Instruct-v0.9-GGUF/blob/main/MaziyarPanahi-Llama-3-8B-Instruct-v0.9-Q6_K.gguf) | 6290.44 MB |
| Q8_0 | [MaziyarPanahi-Llama-3-8B-Instruct-v0.9-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/MaziyarPanahi-Llama-3-8B-Instruct-v0.9-GGUF/blob/main/MaziyarPanahi-Llama-3-8B-Instruct-v0.9-Q8_0.gguf) | 8145.11 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/macadeliccc-WestLake-7B-v2-laser-truthy-dpo-GGUF
|
featherless-ai-quants
| 2024-11-10T19:37:07Z | 24 | 0 | null |
[
"gguf",
"text-generation",
"base_model:macadeliccc/WestLake-7B-v2-laser-truthy-dpo",
"base_model:quantized:macadeliccc/WestLake-7B-v2-laser-truthy-dpo",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-05T00:55:52Z |
---
base_model: macadeliccc/WestLake-7B-v2-laser-truthy-dpo
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# macadeliccc/WestLake-7B-v2-laser-truthy-dpo GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [macadeliccc-WestLake-7B-v2-laser-truthy-dpo-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/macadeliccc-WestLake-7B-v2-laser-truthy-dpo-GGUF/blob/main/macadeliccc-WestLake-7B-v2-laser-truthy-dpo-IQ4_XS.gguf) | 3761.66 MB |
| Q2_K | [macadeliccc-WestLake-7B-v2-laser-truthy-dpo-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/macadeliccc-WestLake-7B-v2-laser-truthy-dpo-GGUF/blob/main/macadeliccc-WestLake-7B-v2-laser-truthy-dpo-Q2_K.gguf) | 2593.27 MB |
| Q3_K_L | [macadeliccc-WestLake-7B-v2-laser-truthy-dpo-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/macadeliccc-WestLake-7B-v2-laser-truthy-dpo-GGUF/blob/main/macadeliccc-WestLake-7B-v2-laser-truthy-dpo-Q3_K_L.gguf) | 3644.97 MB |
| Q3_K_M | [macadeliccc-WestLake-7B-v2-laser-truthy-dpo-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/macadeliccc-WestLake-7B-v2-laser-truthy-dpo-GGUF/blob/main/macadeliccc-WestLake-7B-v2-laser-truthy-dpo-Q3_K_M.gguf) | 3355.97 MB |
| Q3_K_S | [macadeliccc-WestLake-7B-v2-laser-truthy-dpo-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/macadeliccc-WestLake-7B-v2-laser-truthy-dpo-GGUF/blob/main/macadeliccc-WestLake-7B-v2-laser-truthy-dpo-Q3_K_S.gguf) | 3017.97 MB |
| Q4_K_M | [macadeliccc-WestLake-7B-v2-laser-truthy-dpo-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/macadeliccc-WestLake-7B-v2-laser-truthy-dpo-GGUF/blob/main/macadeliccc-WestLake-7B-v2-laser-truthy-dpo-Q4_K_M.gguf) | 4166.07 MB |
| Q4_K_S | [macadeliccc-WestLake-7B-v2-laser-truthy-dpo-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/macadeliccc-WestLake-7B-v2-laser-truthy-dpo-GGUF/blob/main/macadeliccc-WestLake-7B-v2-laser-truthy-dpo-Q4_K_S.gguf) | 3948.57 MB |
| Q5_K_M | [macadeliccc-WestLake-7B-v2-laser-truthy-dpo-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/macadeliccc-WestLake-7B-v2-laser-truthy-dpo-GGUF/blob/main/macadeliccc-WestLake-7B-v2-laser-truthy-dpo-Q5_K_M.gguf) | 4893.69 MB |
| Q5_K_S | [macadeliccc-WestLake-7B-v2-laser-truthy-dpo-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/macadeliccc-WestLake-7B-v2-laser-truthy-dpo-GGUF/blob/main/macadeliccc-WestLake-7B-v2-laser-truthy-dpo-Q5_K_S.gguf) | 4766.19 MB |
| Q6_K | [macadeliccc-WestLake-7B-v2-laser-truthy-dpo-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/macadeliccc-WestLake-7B-v2-laser-truthy-dpo-GGUF/blob/main/macadeliccc-WestLake-7B-v2-laser-truthy-dpo-Q6_K.gguf) | 5666.80 MB |
| Q8_0 | [macadeliccc-WestLake-7B-v2-laser-truthy-dpo-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/macadeliccc-WestLake-7B-v2-laser-truthy-dpo-GGUF/blob/main/macadeliccc-WestLake-7B-v2-laser-truthy-dpo-Q8_0.gguf) | 7339.34 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-GGUF
|
featherless-ai-quants
| 2024-11-10T19:37:05Z | 23 | 0 | null |
[
"gguf",
"text-generation",
"base_model:Locutusque/OpenCerebrum-1.5-Mistral-7B-v0.2-beta",
"base_model:quantized:Locutusque/OpenCerebrum-1.5-Mistral-7B-v0.2-beta",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-05T00:27:38Z |
---
base_model: Locutusque/OpenCerebrum-1.5-Mistral-7B-v0.2-beta
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# Locutusque/OpenCerebrum-1.5-Mistral-7B-v0.2-beta GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-GGUF/blob/main/Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-IQ4_XS.gguf) | 3761.66 MB |
| Q2_K | [Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-GGUF/blob/main/Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-Q2_K.gguf) | 2593.27 MB |
| Q3_K_L | [Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-GGUF/blob/main/Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-Q3_K_L.gguf) | 3644.97 MB |
| Q3_K_M | [Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-GGUF/blob/main/Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-Q3_K_M.gguf) | 3355.97 MB |
| Q3_K_S | [Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-GGUF/blob/main/Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-Q3_K_S.gguf) | 3017.97 MB |
| Q4_K_M | [Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-GGUF/blob/main/Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-Q4_K_M.gguf) | 4166.07 MB |
| Q4_K_S | [Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-GGUF/blob/main/Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-Q4_K_S.gguf) | 3948.57 MB |
| Q5_K_M | [Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-GGUF/blob/main/Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-Q5_K_M.gguf) | 4893.69 MB |
| Q5_K_S | [Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-GGUF/blob/main/Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-Q5_K_S.gguf) | 4766.19 MB |
| Q6_K | [Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-GGUF/blob/main/Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-Q6_K.gguf) | 5666.80 MB |
| Q8_0 | [Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-GGUF/blob/main/Locutusque-OpenCerebrum-1.5-Mistral-7B-v0.2-beta-Q8_0.gguf) | 7339.34 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/jondurbin-bagel-7b-v0.1-GGUF
|
featherless-ai-quants
| 2024-11-10T19:37:02Z | 24 | 0 | null |
[
"gguf",
"text-generation",
"base_model:jondurbin/bagel-7b-v0.1",
"base_model:quantized:jondurbin/bagel-7b-v0.1",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-05T00:05:33Z |
---
base_model: jondurbin/bagel-7b-v0.1
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# jondurbin/bagel-7b-v0.1 GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [jondurbin-bagel-7b-v0.1-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/jondurbin-bagel-7b-v0.1-GGUF/blob/main/jondurbin-bagel-7b-v0.1-IQ4_XS.gguf) | 3761.66 MB |
| Q2_K | [jondurbin-bagel-7b-v0.1-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/jondurbin-bagel-7b-v0.1-GGUF/blob/main/jondurbin-bagel-7b-v0.1-Q2_K.gguf) | 2593.27 MB |
| Q3_K_L | [jondurbin-bagel-7b-v0.1-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/jondurbin-bagel-7b-v0.1-GGUF/blob/main/jondurbin-bagel-7b-v0.1-Q3_K_L.gguf) | 3644.97 MB |
| Q3_K_M | [jondurbin-bagel-7b-v0.1-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/jondurbin-bagel-7b-v0.1-GGUF/blob/main/jondurbin-bagel-7b-v0.1-Q3_K_M.gguf) | 3355.97 MB |
| Q3_K_S | [jondurbin-bagel-7b-v0.1-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/jondurbin-bagel-7b-v0.1-GGUF/blob/main/jondurbin-bagel-7b-v0.1-Q3_K_S.gguf) | 3017.97 MB |
| Q4_K_M | [jondurbin-bagel-7b-v0.1-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/jondurbin-bagel-7b-v0.1-GGUF/blob/main/jondurbin-bagel-7b-v0.1-Q4_K_M.gguf) | 4166.07 MB |
| Q4_K_S | [jondurbin-bagel-7b-v0.1-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/jondurbin-bagel-7b-v0.1-GGUF/blob/main/jondurbin-bagel-7b-v0.1-Q4_K_S.gguf) | 3948.57 MB |
| Q5_K_M | [jondurbin-bagel-7b-v0.1-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/jondurbin-bagel-7b-v0.1-GGUF/blob/main/jondurbin-bagel-7b-v0.1-Q5_K_M.gguf) | 4893.69 MB |
| Q5_K_S | [jondurbin-bagel-7b-v0.1-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/jondurbin-bagel-7b-v0.1-GGUF/blob/main/jondurbin-bagel-7b-v0.1-Q5_K_S.gguf) | 4766.19 MB |
| Q6_K | [jondurbin-bagel-7b-v0.1-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/jondurbin-bagel-7b-v0.1-GGUF/blob/main/jondurbin-bagel-7b-v0.1-Q6_K.gguf) | 5666.80 MB |
| Q8_0 | [jondurbin-bagel-7b-v0.1-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/jondurbin-bagel-7b-v0.1-GGUF/blob/main/jondurbin-bagel-7b-v0.1-Q8_0.gguf) | 7339.34 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/Locutusque-Mistral-7B-SFT-GGUF
|
featherless-ai-quants
| 2024-11-10T19:36:52Z | 18 | 0 | null |
[
"gguf",
"text-generation",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-04T23:24:08Z |
---
base_model: Locutusque/Mistral-7B-SFT
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# Locutusque/Mistral-7B-SFT GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [Locutusque-Mistral-7B-SFT-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Mistral-7B-SFT-GGUF/blob/main/Locutusque-Mistral-7B-SFT-IQ4_XS.gguf) | 3761.66 MB |
| Q2_K | [Locutusque-Mistral-7B-SFT-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Mistral-7B-SFT-GGUF/blob/main/Locutusque-Mistral-7B-SFT-Q2_K.gguf) | 2593.27 MB |
| Q3_K_L | [Locutusque-Mistral-7B-SFT-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Mistral-7B-SFT-GGUF/blob/main/Locutusque-Mistral-7B-SFT-Q3_K_L.gguf) | 3644.97 MB |
| Q3_K_M | [Locutusque-Mistral-7B-SFT-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Mistral-7B-SFT-GGUF/blob/main/Locutusque-Mistral-7B-SFT-Q3_K_M.gguf) | 3355.97 MB |
| Q3_K_S | [Locutusque-Mistral-7B-SFT-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Mistral-7B-SFT-GGUF/blob/main/Locutusque-Mistral-7B-SFT-Q3_K_S.gguf) | 3017.97 MB |
| Q4_K_M | [Locutusque-Mistral-7B-SFT-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Mistral-7B-SFT-GGUF/blob/main/Locutusque-Mistral-7B-SFT-Q4_K_M.gguf) | 4166.07 MB |
| Q4_K_S | [Locutusque-Mistral-7B-SFT-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Mistral-7B-SFT-GGUF/blob/main/Locutusque-Mistral-7B-SFT-Q4_K_S.gguf) | 3948.57 MB |
| Q5_K_M | [Locutusque-Mistral-7B-SFT-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Mistral-7B-SFT-GGUF/blob/main/Locutusque-Mistral-7B-SFT-Q5_K_M.gguf) | 4893.69 MB |
| Q5_K_S | [Locutusque-Mistral-7B-SFT-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Mistral-7B-SFT-GGUF/blob/main/Locutusque-Mistral-7B-SFT-Q5_K_S.gguf) | 4766.19 MB |
| Q6_K | [Locutusque-Mistral-7B-SFT-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Mistral-7B-SFT-GGUF/blob/main/Locutusque-Mistral-7B-SFT-Q6_K.gguf) | 5666.80 MB |
| Q8_0 | [Locutusque-Mistral-7B-SFT-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/Locutusque-Mistral-7B-SFT-GGUF/blob/main/Locutusque-Mistral-7B-SFT-Q8_0.gguf) | 7339.34 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
featherless-ai-quants/jondurbin-airoboros-m-7b-3.1.2-GGUF
|
featherless-ai-quants
| 2024-11-10T19:36:44Z | 30 | 0 | null |
[
"gguf",
"text-generation",
"base_model:jondurbin/airoboros-m-7b-3.1.2",
"base_model:quantized:jondurbin/airoboros-m-7b-3.1.2",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-04T20:29:25Z |
---
base_model: jondurbin/airoboros-m-7b-3.1.2
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# jondurbin/airoboros-m-7b-3.1.2 GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [jondurbin-airoboros-m-7b-3.1.2-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/jondurbin-airoboros-m-7b-3.1.2-GGUF/blob/main/jondurbin-airoboros-m-7b-3.1.2-IQ4_XS.gguf) | 3761.66 MB |
| Q2_K | [jondurbin-airoboros-m-7b-3.1.2-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/jondurbin-airoboros-m-7b-3.1.2-GGUF/blob/main/jondurbin-airoboros-m-7b-3.1.2-Q2_K.gguf) | 2593.27 MB |
| Q3_K_L | [jondurbin-airoboros-m-7b-3.1.2-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/jondurbin-airoboros-m-7b-3.1.2-GGUF/blob/main/jondurbin-airoboros-m-7b-3.1.2-Q3_K_L.gguf) | 3644.97 MB |
| Q3_K_M | [jondurbin-airoboros-m-7b-3.1.2-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/jondurbin-airoboros-m-7b-3.1.2-GGUF/blob/main/jondurbin-airoboros-m-7b-3.1.2-Q3_K_M.gguf) | 3355.97 MB |
| Q3_K_S | [jondurbin-airoboros-m-7b-3.1.2-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/jondurbin-airoboros-m-7b-3.1.2-GGUF/blob/main/jondurbin-airoboros-m-7b-3.1.2-Q3_K_S.gguf) | 3017.97 MB |
| Q4_K_M | [jondurbin-airoboros-m-7b-3.1.2-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/jondurbin-airoboros-m-7b-3.1.2-GGUF/blob/main/jondurbin-airoboros-m-7b-3.1.2-Q4_K_M.gguf) | 4166.07 MB |
| Q4_K_S | [jondurbin-airoboros-m-7b-3.1.2-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/jondurbin-airoboros-m-7b-3.1.2-GGUF/blob/main/jondurbin-airoboros-m-7b-3.1.2-Q4_K_S.gguf) | 3948.57 MB |
| Q5_K_M | [jondurbin-airoboros-m-7b-3.1.2-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/jondurbin-airoboros-m-7b-3.1.2-GGUF/blob/main/jondurbin-airoboros-m-7b-3.1.2-Q5_K_M.gguf) | 4893.69 MB |
| Q5_K_S | [jondurbin-airoboros-m-7b-3.1.2-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/jondurbin-airoboros-m-7b-3.1.2-GGUF/blob/main/jondurbin-airoboros-m-7b-3.1.2-Q5_K_S.gguf) | 4766.19 MB |
| Q6_K | [jondurbin-airoboros-m-7b-3.1.2-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/jondurbin-airoboros-m-7b-3.1.2-GGUF/blob/main/jondurbin-airoboros-m-7b-3.1.2-Q6_K.gguf) | 5666.80 MB |
| Q8_0 | [jondurbin-airoboros-m-7b-3.1.2-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/jondurbin-airoboros-m-7b-3.1.2-GGUF/blob/main/jondurbin-airoboros-m-7b-3.1.2-Q8_0.gguf) | 7339.34 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
prithivMLmods/Pastel-BG-Flux-LoRA
|
prithivMLmods
| 2024-11-10T19:36:33Z | 650 | 14 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"Pastel",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2024-11-10T19:26:57Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
- Pastel
widget:
- text: 'Pastel BG, a young woman with brown hair and blue eyes stands in front of a colorful backdrop. The womans face is adorned with freckles, adding a pop of color to her outfit. The backdrop is a vibrant shade of purple, with yellow stars and stripes on it.'
output:
url: images/PB1.png
- text: 'Pastel BG, An eye-level view of a gray tabby cat with long white whiskers and a pink nose. The cats head is tilted slightly to the right, and its eyes are wide open. Its ears are pointed up, and the cats fur is a mix of gray and black. The background is a combination of pink, purple, and yellow, with white dots dotting the background. To the left of the cat, there is a purple star with a white butterfly on it.'
output:
url: images/PB2.png
- text: 'Pastel BG, a man stands in front of a colorful backdrop. He is dressed in a light pink suit jacket, a yellow collared shirt, and a pair of sunglasses. His hair is styled in a short bob, and his eyes are slightly open. His lips are slightly parted, as if he is looking to the right. The backdrop is a combination of pink, yellow, and green, with small white stars on the right side of the wall.'
output:
url: images/PB3.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: Pastel BG
license: creativeml-openrail-m
---
# Pastel-BG-Flux-LoRA
<Gallery />
- Hosted Here🧨: https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC
**The model is still in the training phase. This is not the final version and may contain artifacts and perform poorly in some cases.**
## Model description
**prithivMLmods/Pastel-BG-Flux-LoRA**
Image Processing Parameters
| Parameter | Value | Parameter | Value |
|---------------------------|--------|---------------------------|--------|
| LR Scheduler | constant | Noise Offset | 0.03 |
| Optimizer | AdamW | Multires Noise Discount | 0.1 |
| Network Dim | 64 | Multires Noise Iterations | 10 |
| Network Alpha | 32 | Repeat & Steps | 28 & 3340|
| Epoch | 15 | Save Every N Epochs | 1 |
Labeling: florence2-en(natural language & English)
Total Images Used for Training : 18 [ Hi-RES ]
## Best Dimensions
- 1024 x 1024 (Default)
## Setting Up
```
import torch
from pipelines import DiffusionPipeline
base_model = "black-forest-labs/FLUX.1-dev"
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
lora_repo = "prithivMLmods/Pastel-BG-Flux-LoRA"
trigger_word = "Pastel BG"
pipe.load_lora_weights(lora_repo)
device = torch.device("cuda")
pipe.to(device)
```
## Trigger words
You should use `Pastel BG` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/prithivMLmods/Pastel-BG-Flux-LoRA/tree/main) them in the Files & versions tab.
|
featherless-ai-quants/FallenMerick-Chunky-Lemon-Cookie-11B-GGUF
|
featherless-ai-quants
| 2024-11-10T19:31:49Z | 24 | 1 | null |
[
"gguf",
"text-generation",
"base_model:FallenMerick/Chunky-Lemon-Cookie-11B",
"base_model:quantized:FallenMerick/Chunky-Lemon-Cookie-11B",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-05T08:11:37Z |
---
base_model: FallenMerick/Chunky-Lemon-Cookie-11B
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# FallenMerick/Chunky-Lemon-Cookie-11B GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [FallenMerick-Chunky-Lemon-Cookie-11B-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/FallenMerick-Chunky-Lemon-Cookie-11B-GGUF/blob/main/FallenMerick-Chunky-Lemon-Cookie-11B-IQ4_XS.gguf) | 5557.67 MB |
| Q2_K | [FallenMerick-Chunky-Lemon-Cookie-11B-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/FallenMerick-Chunky-Lemon-Cookie-11B-GGUF/blob/main/FallenMerick-Chunky-Lemon-Cookie-11B-Q2_K.gguf) | 3817.78 MB |
| Q3_K_L | [FallenMerick-Chunky-Lemon-Cookie-11B-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/FallenMerick-Chunky-Lemon-Cookie-11B-GGUF/blob/main/FallenMerick-Chunky-Lemon-Cookie-11B-Q3_K_L.gguf) | 5388.98 MB |
| Q3_K_M | [FallenMerick-Chunky-Lemon-Cookie-11B-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/FallenMerick-Chunky-Lemon-Cookie-11B-GGUF/blob/main/FallenMerick-Chunky-Lemon-Cookie-11B-Q3_K_M.gguf) | 4954.98 MB |
| Q3_K_S | [FallenMerick-Chunky-Lemon-Cookie-11B-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/FallenMerick-Chunky-Lemon-Cookie-11B-GGUF/blob/main/FallenMerick-Chunky-Lemon-Cookie-11B-Q3_K_S.gguf) | 4448.48 MB |
| Q4_K_M | [FallenMerick-Chunky-Lemon-Cookie-11B-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/FallenMerick-Chunky-Lemon-Cookie-11B-GGUF/blob/main/FallenMerick-Chunky-Lemon-Cookie-11B-Q4_K_M.gguf) | 6162.33 MB |
| Q4_K_S | [FallenMerick-Chunky-Lemon-Cookie-11B-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/FallenMerick-Chunky-Lemon-Cookie-11B-GGUF/blob/main/FallenMerick-Chunky-Lemon-Cookie-11B-Q4_K_S.gguf) | 5835.08 MB |
| Q5_K_M | [FallenMerick-Chunky-Lemon-Cookie-11B-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/FallenMerick-Chunky-Lemon-Cookie-11B-GGUF/blob/main/FallenMerick-Chunky-Lemon-Cookie-11B-Q5_K_M.gguf) | 7245.95 MB |
| Q5_K_S | [FallenMerick-Chunky-Lemon-Cookie-11B-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/FallenMerick-Chunky-Lemon-Cookie-11B-GGUF/blob/main/FallenMerick-Chunky-Lemon-Cookie-11B-Q5_K_S.gguf) | 7054.70 MB |
| Q6_K | [FallenMerick-Chunky-Lemon-Cookie-11B-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/FallenMerick-Chunky-Lemon-Cookie-11B-GGUF/blob/main/FallenMerick-Chunky-Lemon-Cookie-11B-Q6_K.gguf) | 8397.30 MB |
| Q8_0 | [FallenMerick-Chunky-Lemon-Cookie-11B-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/FallenMerick-Chunky-Lemon-Cookie-11B-GGUF/blob/main/FallenMerick-Chunky-Lemon-Cookie-11B-Q8_0.gguf) | 10875.85 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
phogen/FineLlama-3.1-8B_instruct
|
phogen
| 2024-11-10T19:22:31Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/Meta-Llama-3.1-8B-Instruct",
"base_model:finetune:unsloth/Meta-Llama-3.1-8B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-10T19:18:41Z |
---
base_model: unsloth/Meta-Llama-3.1-8B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** phogen
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Meta-Llama-3.1-8B-Instruct
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
waloneai/mawc-cc
|
waloneai
| 2024-11-10T19:20:21Z | 189 | 0 |
diffusers
|
[
"diffusers",
"flux",
"text-to-image",
"lora",
"fal",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2024-11-10T19:20:18Z |
---
tags:
- flux
- text-to-image
- lora
- diffusers
- fal
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: mawc
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
# mawc cc
<Gallery />
## Model description
## Trigger words
You should use `mawc` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/shweaung/mawc-cc/tree/main) them in the Files & versions tab.
## Training at fal.ai
Training was done using [fal.ai/models/fal-ai/flux-lora-fast-training](https://fal.ai/models/fal-ai/flux-lora-fast-training).
|
zelk12/MT-Gen2-BB-gemma-2-MTMMT2-9B
|
zelk12
| 2024-11-10T19:14:32Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma2",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:zelk12/MT-Merge-gemma-2-9B",
"base_model:merge:zelk12/MT-Merge-gemma-2-9B",
"base_model:zelk12/MT2-gemma-2-9B",
"base_model:merge:zelk12/MT2-gemma-2-9B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-10T19:08:18Z |
---
base_model:
- zelk12/MT2-gemma-2-9B
- zelk12/MT-Merge-gemma-2-9B
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [zelk12/MT2-gemma-2-9B](https://huggingface.co/zelk12/MT2-gemma-2-9B)
* [zelk12/MT-Merge-gemma-2-9B](https://huggingface.co/zelk12/MT-Merge-gemma-2-9B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: zelk12/MT-Merge-gemma-2-9B
- model: zelk12/MT2-gemma-2-9B
merge_method: slerp
base_model: zelk12/MT-Merge-gemma-2-9B
dtype: bfloat16
parameters:
t: 0.25
```
|
jacobhoffmann/TestGen_v2.1-codegemma-7b
|
jacobhoffmann
| 2024-11-10T18:59:21Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-10T18:54:17Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mav23/mistral-rrc-GGUF
|
mav23
| 2024-11-10T18:55:21Z | 184 | 0 | null |
[
"gguf",
"legal",
"housing",
"covenants",
"property",
"deed",
"racial-covenant",
"en",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:quantized:mistralai/Mistral-7B-v0.1",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-11-10T17:56:45Z |
---
license: mit
language:
- en
base_model:
- mistralai/Mistral-7B-v0.1
tags:
- legal
- housing
- covenants
- property
- deed
- racial-covenant
---
# reglab-rrc/mistral-rrc
**Paper:** [AI for Scaling Legal Reform: Mapping and Redacting Racial Covenants in Santa Clara County](https://reglab.github.io/racialcovenants)
**Overview of Model Details**
* Model name: `reglab-rrc/mistral-rrc`
* Version: 1.0
* Release date: October 17, 2024
* Model type: Finetuned causal language model (Mistral 7B)
* License: Open-source, licensed under the MIT License
* Language: English
Domains: Legal documents (real property deeds)
* Task: Text classification and extraction (racial covenant detection)
## Usage
Here is an example of how to use the model to find racial covenants in a page of a deed:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import re
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("reglab/mistral-rrc")
model = AutoModelForCausalLM.from_pretrained("reglab/mistral-rrc")
def format_prompt(document):
return f"""### Instruction:
Determine whether the property deed contains a racial covenant. A racial covenant is a clause in a document that \
restricts who can reside, own, or occupy a property on the basis of race, ethnicity, national origin, or religion. \
Answer "Yes" or "No". If "Yes", provide the exact text of the relevant passage and then a quotation of the passage \
with spelling and formatting errors fixed.
### Input:
{document}
### Response:"""
def parse_output(output):
answer_match = re.search(r"\[ANSWER\](.*?)\[/ANSWER\]", output, re.DOTALL)
raw_passage_match = re.search(r"\[RAW PASSAGE\](.*?)\[/RAW PASSAGE\]", output, re.DOTALL)
quotation_match = re.search(r"\[CORRECTED QUOTATION\](.*?)\[/CORRECTED QUOTATION\]", output, re.DOTALL)
answer = answer_match.group(1).strip() if answer_match else None
raw_passage = raw_passage_match.group(1).strip() if raw_passage_match else None
quotation = quotation_match.group(1).strip() if quotation_match else None
return {
"answer": answer == "Yes",
"raw_passage": raw_passage,
"quotation": quotation
}
# Example usage
document = "[[Your property deed text here...]]"
prompt = format_prompt(document)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
result = tokenizer.decode(outputs[0])
parsed_result = parse_output(result)
print(parsed_result)
```
## Input and Output Formats
The model was trained with the input and output formats above, so please make sure to use these formats
when running inference.
- **Input Format:** The model accepts property deed documents in text format. It expects properly formatted prompts based on the instructional format outlined in the usage example, including the instruction to detect racial covenants and provide corrected text if found.
- **Output Format:** The output includes a response that provides:
- An answer to whether a racial covenant is present ("Yes" or "No").
- The raw text of the racial covenant if detected.
- A corrected quotation of the racial covenant text with spelling and formatting errors fixed.
## Intended Use
The finetuned Mistral model (`reglab-rrc/mistral-rrc`) is designed to detect and extract racial covenants from property deed documents. Racial covenants are clauses that historically restricted property ownership or residence based on race, ethnicity, national origin, or religion. This model aims to aid jurisdictions, such as Santa Clara County (CA), in identifying these covenants for removal or redaction, as mandated by laws like California's AB 1466. The intended use is to prioritize documents for review, reducing the time and resources required for human auditors to locate RRCs manually, particularly in large datasets of property deeds. Legal professionals and government entities can integrate the model into workflows to streamline and scale up the process of identifying racially discriminatory language in real estate records.
---
## Training Data
The Mistral 7B model was finetuned on a collection of property deed documents gathered from eight counties across the United States, including Santa Clara County (CA). To account for potential variations in document formatting, OCR quality, and phrasing, data augmentation included property deeds from other jurisdictions, such as Bexar County (TX), Cuyahoga County (OH), and Hidalgo County (TX). In total, the training dataset comprised 3,801 annotated deed pages, with 2,987 (78.6%) containing racially restrictive covenants. The dataset was balanced with both positive and negative examples, derived from keyword-based searches and manual annotation efforts. The data was annotated through a multi-stage process, which included manual verification of model predictions and the development of a web-based annotation tool for more efficient data labeling. (For additional details about data augmentation and training, please refer to our paper.)
---
## Performance
The finetuned model was evaluated on a held-out test set of 739 pages from the original dataset, with approximately 70% of these pages containing racial covenants. Performance metrics for the model include page-level precision, recall, and F1 score, as well as span-level BLEU scores, to measure how accurately the model reproduced the exact span of the detected covenant text. The results are as follows:
- **Precision:** 1.000 (95% CI: 0.995-1.000)
- **Recall:** 0.994 (95% CI: 0.984-0.997)
- **F1 score:** 0.997
- **BLEU score:** 0.932 (for span-level accuracy of detected covenants)
The finetuned Mistral model outperformed other approaches, including keyword and fuzzy matching as well as zero-shot and few-shot GPT models, particularly in recall and precision.
---
### Limitations
Despite the performance of the finetuned Mistral model in detecting racial covenants, several limitations remain that must be considered and stated:
1. **Generalizability Across Jurisdictions:** This model was primarily finetuned on property deeds from eight counties, including Bexar County (TX), Cuyahoga County (OH), and Santa Clara County (CA). While we took care to include a variety of document types and OCR qualities, property deed language and formatting can vary significantly by jurisdiction. As a result, the model's performance may degrade when applied to regions with distinct linguistic, legal, or historical document structures. Future efforts should include jurisdiction-specific validation to ensure accurate detection in areas with unique property deed formats.
2. **Sensitivity to OCR Artifacts:** Although the model is robust to many types of OCR (optical character recognition) errors, heavily degraded documents or those with extremely poor scan quality may still pose challenges. Scanning artifacts can introduce noise that obscures key terms, leading to either missed racial covenants (false negatives) or incorrect detections (false positives). This remains a potential source of error, particularly in counties with older, handwritten, or poorly preserved records.
3. **Contextual Ambiguity:** The model relies on semantic analysis to identify racial covenants, and while this enhances its ability to detect atypical language, some ambiguity remains. For instance, terms like "white" could refer to a racial category or a person's name, and the model's ability to disambiguate such terms is not perfect, especially in cases where poor scanning quality makes it difficult to distinguish the usage of the ambigious term based on the semantic content of the deed. In such cases, legal professionals must still verify the results, ensuring no improper redactions or omissions occur.
4. **Historical Document Complexity:** The language used in older property deeds can be complex and archaic. Some racial covenants may be expressed in subtle or convoluted ways that could evade even the most advanced language models. While the model has shown strong performance in capturing most covenants, human oversight remains crucial, particularly for documents with unusual or legally obscure phrasing.
5. **Dependency on Human Review:** Although the model reduces the manual work pretty significantly, legal review is still required for final verification. This human-in-the-loop approach mitigates the risk of false positives, but it does not entirely eliminate the need for expert intervention, particularly in the redaction and historical preservation processes.
---
### Ethical Considerations
The deployment of a language model for detecting racial covenants raises several important ethical considerations. We have done our best to carefully address these concerns throughout the project:
1. **Preservation of Historical Memory:** A key ethical consideration in this project is balancing the removal of offensive language from property deeds with the need to preserve historical records. While the model identifies and assists in redacting racially restrictive covenants, these covenants are also preserved in a historical registry by the County. This ensures that the history of housing discrimination is not erased but documented and made accessible for future research and public awareness. The creation of this historical record serves as an educational tool to understand the deep and troubling legacy of racial exclusion in housing markets.
2. **Accountability and Oversight:** The system has been designed with a clear chain of accountability, as required by California’s AB 1466. All flagged documents must undergo legal review, ensuring that no inappropriate redactions occur and that the process is transparent and accurate. This human oversight safeguards against over-reliance on automated systems, which, while highly effective, are not infallible. Our current AI-driven pipeline prioritizes documents for review, but final decisions rest with human experts (most specifically, legal professionals), mitigating the risk of both false positives and false negatives.
3. **Bias and Fairness:** The model is trained on historical documents that reflect the racial and social biases of the time. While the model itself is neutral in its detection of racially restrictive language, the training data may inherently carry these biases, as they originate from a time when discriminatory covenants were legally permissible. Ongoing efforts are required to ensure that the model does not perpetuate unintended biases, especially in jurisdictions with different historical contexts. Regular validation across diverse datasets and jurisdictions is essential to prevent any unfair outcomes.
4. **Accessibility and Open Model:** By choosing to finetune an open-source model (Mistral 7B), this project has prioritized transparency and accessibility. This decision makes the technology available to smaller counties and community-based organizations, many of which lack the resources to develop or license proprietary solutions. The release of the model empowers a broader range of actors to engage in legal reform efforts, fostering greater equity in the identification and removal of racial covenants. Additionally, privacy concerns have been addressed by masking private information in the training data, ensuring that the model does not learn or reproduce sensitive data.
5. **Advancing Public Good:** This project exemplifies how AI can be leveraged for the public good. By revealing patterns of housing discrimination and aiding in legal reform, the model contributes to ongoing efforts to address historical injustices. Beyond merely automating a legal task, this project enhances our understanding of systemic racism in the housing market, adding valuable insights to the academic and public discourse. It is a powerful illustration of how technology can assist in the pursuit of justice, equity, and historical accountability.
## Citation
If your work makes use of our model, data, or results, we request that you cite our paper as follows:
```bibtex
@article{suranisuzgun2024,
title={AI for Scaling Legal Reform: Mapping and Redacting Racial Covenants in Santa Clara County},
author={Surani, Faiz and Suzgun, Mirac and Raman, Vyoma and Manning, Christopher D. and Henderson, Peter and Ho, Daniel E.},
url={https://dho.stanford.edu/wp-content/uploads/Covenants.pdf},
year={2024}
}
```
|
mradermacher/gemma-7b-openhermes-i1-GGUF
|
mradermacher
| 2024-11-10T18:54:09Z | 92 | 1 |
transformers
|
[
"transformers",
"gguf",
"generated_from_trainer",
"axolotl",
"gemma",
"instruct",
"finetune",
"chatml",
"gpt4",
"synthetic data",
"distillation",
"en",
"dataset:mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha",
"base_model:abideen/gemma-7b-openhermes",
"base_model:quantized:abideen/gemma-7b-openhermes",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-11-10T13:30:20Z |
---
base_model: abideen/gemma-7b-openhermes
datasets:
- mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha
language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
tags:
- generated_from_trainer
- axolotl
- gemma
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- distillation
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/abideen/gemma-7b-openhermes
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/gemma-7b-openhermes-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-openhermes-i1-GGUF/resolve/main/gemma-7b-openhermes.i1-IQ1_S.gguf) | i1-IQ1_S | 2.3 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-openhermes-i1-GGUF/resolve/main/gemma-7b-openhermes.i1-IQ1_M.gguf) | i1-IQ1_M | 2.4 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-openhermes-i1-GGUF/resolve/main/gemma-7b-openhermes.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-openhermes-i1-GGUF/resolve/main/gemma-7b-openhermes.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-openhermes-i1-GGUF/resolve/main/gemma-7b-openhermes.i1-IQ2_S.gguf) | i1-IQ2_S | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-openhermes-i1-GGUF/resolve/main/gemma-7b-openhermes.i1-IQ2_M.gguf) | i1-IQ2_M | 3.2 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-openhermes-i1-GGUF/resolve/main/gemma-7b-openhermes.i1-Q2_K.gguf) | i1-Q2_K | 3.6 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-openhermes-i1-GGUF/resolve/main/gemma-7b-openhermes.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-openhermes-i1-GGUF/resolve/main/gemma-7b-openhermes.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-openhermes-i1-GGUF/resolve/main/gemma-7b-openhermes.i1-IQ3_S.gguf) | i1-IQ3_S | 4.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-openhermes-i1-GGUF/resolve/main/gemma-7b-openhermes.i1-Q3_K_S.gguf) | i1-Q3_K_S | 4.1 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-openhermes-i1-GGUF/resolve/main/gemma-7b-openhermes.i1-IQ3_M.gguf) | i1-IQ3_M | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-openhermes-i1-GGUF/resolve/main/gemma-7b-openhermes.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.5 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-openhermes-i1-GGUF/resolve/main/gemma-7b-openhermes.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.8 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-openhermes-i1-GGUF/resolve/main/gemma-7b-openhermes.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-openhermes-i1-GGUF/resolve/main/gemma-7b-openhermes.i1-Q4_0.gguf) | i1-Q4_0 | 5.1 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-openhermes-i1-GGUF/resolve/main/gemma-7b-openhermes.i1-Q4_K_S.gguf) | i1-Q4_K_S | 5.1 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-openhermes-i1-GGUF/resolve/main/gemma-7b-openhermes.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-openhermes-i1-GGUF/resolve/main/gemma-7b-openhermes.i1-Q5_K_S.gguf) | i1-Q5_K_S | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-openhermes-i1-GGUF/resolve/main/gemma-7b-openhermes.i1-Q5_K_M.gguf) | i1-Q5_K_M | 6.2 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-openhermes-i1-GGUF/resolve/main/gemma-7b-openhermes.i1-Q6_K.gguf) | i1-Q6_K | 7.1 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
phogen/FineLlama-3.1-8B
|
phogen
| 2024-11-10T18:53:04Z | 11 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/Meta-Llama-3.1-8B",
"base_model:finetune:unsloth/Meta-Llama-3.1-8B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-10T18:49:20Z |
---
base_model: unsloth/Meta-Llama-3.1-8B
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** phogen
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Meta-Llama-3.1-8B
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Stable-X/yoso-normal-v1-0
|
Stable-X
| 2024-11-10T18:38:40Z | 1,049 | 1 |
diffusers
|
[
"diffusers",
"image-to-image",
"license:apache-2.0",
"diffusers:YOSONormalsPipeline",
"region:us"
] |
image-to-image
| 2024-10-23T16:58:26Z |
---
library_name: diffusers
pipeline_tag: image-to-image
license: apache-2.0
---
# Model Card for StableNormal
This repository contains the weights of StableNormal: Reducing Diffusion Variance for Stable and Sharp Normal
## Usage
See the Github repository: https://github.com/Stable-X/StableNormal regarding installation instructions.
The model can then be used as follows:
```python
import torch
from PIL import Image
# Load an image
input_image = Image.open("path/to/your/image.jpg")
# Create predictor instance
predictor = torch.hub.load("Stable-X/StableNormal", "StableNormal_turbo", trust_repo=True, yoso_version='yoso-normal-v1-0')
# Apply the model to the image
normal_image = predictor(input_image)
# Save or display the result
normal_image.save("output/normal_map.png")
```
|
mradermacher/internlm-20b-llama-i1-GGUF
|
mradermacher
| 2024-11-10T18:33:12Z | 84 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:KnutJaegersberg/internlm-20b-llama",
"base_model:quantized:KnutJaegersberg/internlm-20b-llama",
"license:other",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-11-10T15:22:15Z |
---
base_model: KnutJaegersberg/internlm-20b-llama
language:
- en
library_name: transformers
license: other
license_link: LICENSE
license_name: internlm
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/KnutJaegersberg/internlm-20b-llama
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/internlm-20b-llama-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/internlm-20b-llama-i1-GGUF/resolve/main/internlm-20b-llama.i1-IQ1_S.gguf) | i1-IQ1_S | 4.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/internlm-20b-llama-i1-GGUF/resolve/main/internlm-20b-llama.i1-IQ1_M.gguf) | i1-IQ1_M | 5.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/internlm-20b-llama-i1-GGUF/resolve/main/internlm-20b-llama.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/internlm-20b-llama-i1-GGUF/resolve/main/internlm-20b-llama.i1-IQ2_XS.gguf) | i1-IQ2_XS | 6.2 | |
| [GGUF](https://huggingface.co/mradermacher/internlm-20b-llama-i1-GGUF/resolve/main/internlm-20b-llama.i1-IQ2_S.gguf) | i1-IQ2_S | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/internlm-20b-llama-i1-GGUF/resolve/main/internlm-20b-llama.i1-IQ2_M.gguf) | i1-IQ2_M | 7.2 | |
| [GGUF](https://huggingface.co/mradermacher/internlm-20b-llama-i1-GGUF/resolve/main/internlm-20b-llama.i1-Q2_K.gguf) | i1-Q2_K | 7.7 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/internlm-20b-llama-i1-GGUF/resolve/main/internlm-20b-llama.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 7.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/internlm-20b-llama-i1-GGUF/resolve/main/internlm-20b-llama.i1-IQ3_XS.gguf) | i1-IQ3_XS | 8.5 | |
| [GGUF](https://huggingface.co/mradermacher/internlm-20b-llama-i1-GGUF/resolve/main/internlm-20b-llama.i1-IQ3_S.gguf) | i1-IQ3_S | 8.9 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/internlm-20b-llama-i1-GGUF/resolve/main/internlm-20b-llama.i1-Q3_K_S.gguf) | i1-Q3_K_S | 8.9 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/internlm-20b-llama-i1-GGUF/resolve/main/internlm-20b-llama.i1-IQ3_M.gguf) | i1-IQ3_M | 9.4 | |
| [GGUF](https://huggingface.co/mradermacher/internlm-20b-llama-i1-GGUF/resolve/main/internlm-20b-llama.i1-Q3_K_M.gguf) | i1-Q3_K_M | 10.0 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/internlm-20b-llama-i1-GGUF/resolve/main/internlm-20b-llama.i1-Q3_K_L.gguf) | i1-Q3_K_L | 10.8 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/internlm-20b-llama-i1-GGUF/resolve/main/internlm-20b-llama.i1-IQ4_XS.gguf) | i1-IQ4_XS | 10.9 | |
| [GGUF](https://huggingface.co/mradermacher/internlm-20b-llama-i1-GGUF/resolve/main/internlm-20b-llama.i1-Q4_0.gguf) | i1-Q4_0 | 11.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/internlm-20b-llama-i1-GGUF/resolve/main/internlm-20b-llama.i1-Q4_K_S.gguf) | i1-Q4_K_S | 11.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/internlm-20b-llama-i1-GGUF/resolve/main/internlm-20b-llama.i1-Q4_K_M.gguf) | i1-Q4_K_M | 12.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/internlm-20b-llama-i1-GGUF/resolve/main/internlm-20b-llama.i1-Q5_K_S.gguf) | i1-Q5_K_S | 14.0 | |
| [GGUF](https://huggingface.co/mradermacher/internlm-20b-llama-i1-GGUF/resolve/main/internlm-20b-llama.i1-Q5_K_M.gguf) | i1-Q5_K_M | 14.4 | |
| [GGUF](https://huggingface.co/mradermacher/internlm-20b-llama-i1-GGUF/resolve/main/internlm-20b-llama.i1-Q6_K.gguf) | i1-Q6_K | 16.6 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
ICT3214-Group5/MD5_gpt_neo_v1.1.5
|
ICT3214-Group5
| 2024-11-10T18:29:35Z | 103 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"base_model:EleutherAI/gpt-neo-125m",
"base_model:finetune:EleutherAI/gpt-neo-125m",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-10T17:54:59Z |
---
library_name: transformers
license: mit
base_model: EleutherAI/gpt-neo-125M
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: MD5_gpt_neo_v1.1.5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MD5_gpt_neo_v1.1.5
This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 8.9359
- Rouge1: 0.0339
- Rouge2: 0.0
- Rougel: 0.0320
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|:------:|
| No log | 0.9947 | 94 | 3.7011 | 0.2262 | 0.1380 | 0.2073 |
| No log | 2.0 | 189 | 9.8120 | 0.0371 | 0.0 | 0.0273 |
| No log | 2.9947 | 283 | 8.9472 | 0.0280 | 0.0 | 0.0249 |
| No log | 4.0 | 378 | 8.7092 | 0.0325 | 0.0 | 0.0264 |
| No log | 4.9735 | 470 | 8.9359 | 0.0339 | 0.0 | 0.0320 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.1
|
mradermacher/RTLCoder-Deepseek-v1.1-i1-GGUF
|
mradermacher
| 2024-11-10T18:26:14Z | 260 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:ishorn5/RTLCoder-Deepseek-v1.1",
"base_model:quantized:ishorn5/RTLCoder-Deepseek-v1.1",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-11-10T15:47:35Z |
---
base_model: ishorn5/RTLCoder-Deepseek-v1.1
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/ishorn5/RTLCoder-Deepseek-v1.1
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-i1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.i1-IQ1_S.gguf) | i1-IQ1_S | 1.6 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-i1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.i1-IQ1_M.gguf) | i1-IQ1_M | 1.8 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-i1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-i1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-i1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.i1-IQ2_S.gguf) | i1-IQ2_S | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-i1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.i1-IQ2_M.gguf) | i1-IQ2_M | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-i1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.i1-Q2_K.gguf) | i1-Q2_K | 2.6 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-i1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-i1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.i1-IQ3_XS.gguf) | i1-IQ3_XS | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-i1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.i1-IQ3_S.gguf) | i1-IQ3_S | 3.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-i1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.1 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-i1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.i1-IQ3_M.gguf) | i1-IQ3_M | 3.2 | |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-i1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-i1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.7 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-i1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.i1-IQ4_XS.gguf) | i1-IQ4_XS | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-i1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 3.9 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-i1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 3.9 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-i1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 3.9 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-i1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.i1-Q4_0.gguf) | i1-Q4_0 | 3.9 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-i1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.0 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-i1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-i1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.i1-Q5_K_S.gguf) | i1-Q5_K_S | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-i1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.i1-Q5_K_M.gguf) | i1-Q5_K_M | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-i1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.i1-Q6_K.gguf) | i1-Q6_K | 5.6 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mradermacher/RTLCoder-Deepseek-v1.1-GGUF
|
mradermacher
| 2024-11-10T18:26:13Z | 327 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:ishorn5/RTLCoder-Deepseek-v1.1",
"base_model:quantized:ishorn5/RTLCoder-Deepseek-v1.1",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-08T21:12:38Z |
---
base_model: ishorn5/RTLCoder-Deepseek-v1.1
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/ishorn5/RTLCoder-Deepseek-v1.1
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.Q2_K.gguf) | Q2_K | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.Q3_K_S.gguf) | Q3_K_S | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.Q3_K_M.gguf) | Q3_K_M | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.Q3_K_L.gguf) | Q3_K_L | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.IQ4_XS.gguf) | IQ4_XS | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.Q4_0_4_4.gguf) | Q4_0_4_4 | 3.9 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.Q4_K_S.gguf) | Q4_K_S | 4.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.Q4_K_M.gguf) | Q4_K_M | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.Q5_K_S.gguf) | Q5_K_S | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.Q5_K_M.gguf) | Q5_K_M | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.Q6_K.gguf) | Q6_K | 5.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.Q8_0.gguf) | Q8_0 | 7.3 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/RTLCoder-Deepseek-v1.1-GGUF/resolve/main/RTLCoder-Deepseek-v1.1.f16.gguf) | f16 | 13.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
Raxixion/goliapplecider
|
Raxixion
| 2024-11-10T18:15:40Z | 5 | 1 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2024-11-10T17:59:47Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: GOLIAPPLECIDER
---
# Goliapplecider
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `GOLIAPPLECIDER` to trigger the image generation.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('Raxixion/goliapplecider', weight_name='lora.safetensors')
image = pipeline('your prompt').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
|
kanishka/opt-babylm2-rewritten-clean-spacy-32k-earlystop_seed-42_3e-4
|
kanishka
| 2024-11-10T18:08:32Z | 136 | 0 |
transformers
|
[
"transformers",
"safetensors",
"opt",
"text-generation",
"generated_from_trainer",
"dataset:kanishka/babylm2-rewritten-clean-spacy",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-10T06:42:25Z |
---
library_name: transformers
tags:
- generated_from_trainer
datasets:
- kanishka/babylm2-rewritten-clean-spacy
metrics:
- accuracy
model-index:
- name: opt-babylm2-rewritten-clean-spacy-32k-earlystop_seed-42_3e-4
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: kanishka/babylm2-rewritten-clean-spacy
type: kanishka/babylm2-rewritten-clean-spacy
metrics:
- name: Accuracy
type: accuracy
value: 0.4242773576186558
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opt-babylm2-rewritten-clean-spacy-32k-earlystop_seed-42_3e-4
This model was trained from scratch on the kanishka/babylm2-rewritten-clean-spacy dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9642
- Accuracy: 0.4243
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 32000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|
| 6.8783 | 0.9996 | 1931 | 4.4654 | 0.2891 |
| 4.2263 | 1.9997 | 3863 | 3.9179 | 0.3337 |
| 3.7724 | 2.9999 | 5795 | 3.6397 | 0.3562 |
| 3.5091 | 4.0 | 7727 | 3.4569 | 0.3724 |
| 3.3306 | 4.9996 | 9658 | 3.3310 | 0.3838 |
| 3.2012 | 5.9997 | 11590 | 3.2469 | 0.3918 |
| 3.1088 | 6.9999 | 13522 | 3.1828 | 0.3982 |
| 3.0364 | 8.0 | 15454 | 3.1404 | 0.4023 |
| 2.9837 | 8.9996 | 17385 | 3.1080 | 0.4057 |
| 2.9377 | 9.9997 | 19317 | 3.0840 | 0.4077 |
| 2.9019 | 10.9999 | 21249 | 3.0633 | 0.4101 |
| 2.8713 | 12.0 | 23181 | 3.0505 | 0.4117 |
| 2.8449 | 12.9996 | 25112 | 3.0376 | 0.4130 |
| 2.8231 | 13.9997 | 27044 | 3.0270 | 0.4143 |
| 2.7828 | 14.9999 | 28976 | 3.0222 | 0.4150 |
| 2.7644 | 16.0 | 30908 | 3.0160 | 0.4156 |
| 2.7508 | 16.9996 | 32839 | 3.0037 | 0.4175 |
| 2.7036 | 17.9997 | 34771 | 2.9802 | 0.4205 |
| 2.6333 | 18.9999 | 36703 | 2.9677 | 0.4231 |
| 2.557 | 19.9922 | 38620 | 2.9642 | 0.4243 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0
|
jacksors/datathon-24-connections
|
jacksors
| 2024-11-10T18:01:09Z | 34 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"facebook",
"meta",
"llama-3",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"arxiv:2204.05149",
"arxiv:2405.16406",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-10T09:57:39Z |
---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
library_name: transformers
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
license: llama3.2
extra_gated_prompt: >-
### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT
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distributed by Meta at https://llama.meta.com/doc/overview.
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entering into this Agreement on such person or entity’s behalf), of the age required under
applicable laws, rules or regulations to provide legal consent and that has legal authority
to bind your employer or such other person or entity if you are entering in this Agreement
on their behalf.
“Llama 3.2” means the foundational large language models and software and algorithms, including
machine-learning model code, trained model weights, inference-enabling code, training-enabling code,
fine-tuning enabling code and other elements of the foregoing distributed by Meta at
https://www.llama.com/llama-downloads.
“Llama Materials” means, collectively, Meta’s proprietary Llama 3.2 and Documentation (and
any portion thereof) made available under this Agreement.
“Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or,
if you are an entity, your principal place of business is in the EEA or Switzerland)
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By clicking “I Accept” below or by using or distributing any portion or element of the Llama Materials,
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1. License Rights and Redistribution.
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### Llama 3.2 Acceptable Use Policy
Meta is committed to promoting safe and fair use of its tools and features, including Llama 3.2.
If you access or use Llama 3.2, you agree to this Acceptable Use Policy (“**Policy**”).
The most recent copy of this policy can be found at
[https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).
#### Prohibited Uses
We want everyone to use Llama 3.2 safely and responsibly. You agree you will not use, or allow others to use, Llama 3.2 to:
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1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
1. Violence or terrorism
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8. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State or to the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical Weapons Convention Implementation Act of 1997
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---
## Model Information
The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks.
**Model Developer:** Meta
**Model Architecture:** Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
| | Training Data | Params | Input modalities | Output modalities | Context Length | GQA | Shared Embeddings | Token count | Knowledge cutoff |
| :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- |
| Llama 3.2 (text only) | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code | 128k | Yes | Yes | Up to 9T tokens | December 2023 |
| | | 3B (3.21B) | Multilingual Text | Multilingual Text and code | | | | | |
| Llama 3.2 Quantized (text only) | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code | 8k | Yes | Yes | Up to 9T tokens | December 2023 |
| | | 3B (3.21B) | Multilingual Text | Multilingual Text and code | | | | | |
**Supported Languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly.
**Llama 3.2 Model Family:** Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date:** Sept 25, 2024
**Status:** This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety.
**License:** Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement).
**Feedback:** Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases:** Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pretrained models can be adapted for a variety of additional natural language generation tasks. Similarly, quantized models can be adapted for a variety of on-device use-cases with limited compute resources.
**Out of Scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card.
## How to use
This repository contains two versions of Llama-3.2-1B-Instruct, for use with transformers and with the original `llama` codebase.
### Use with transformers
Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function.
Make sure to update your transformers installation via `pip install --upgrade transformers`.
```python
import torch
from transformers import pipeline
model_id = "meta-llama/Llama-3.2-1B-Instruct"
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
outputs = pipe(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes)
### Use with `llama`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama)
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Llama-3.2-1B-Instruct --include "original/*" --local-dir Llama-3.2-1B-Instruct
```
## Hardware and Software
**Training Factors:** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure.
**Training Energy Use:** Training utilized a cumulative of **916k** GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency.
**Training Greenhouse Gas Emissions:** Estimated total location-based greenhouse gas emissions were **240** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq.
| | Training Time (GPU hours) | Logit Generation Time (GPU Hours) | Training Power Consumption (W) | Training Location-Based Greenhouse Gas Emissions (tons CO2eq) | Training Market-Based Greenhouse Gas Emissions (tons CO2eq) |
| :---- | :---: | ----- | :---: | :---: | :---: |
| Llama 3.2 1B | 370k | \- | 700 | 107 | 0 |
| Llama 3.2 3B | 460k | \- | 700 | 133 | 0 |
| Llama 3.2 1B SpinQuant | 1.7 | 0 | 700 | *Negligible*\*\* | 0 |
| Llama 3.2 3B SpinQuant | 2.4 | 0 | 700 | *Negligible*\*\* | 0 |
| Llama 3.2 1B QLora | 1.3k | 0 | 700 | 0.381 | 0 |
| Llama 3.2 3B QLora | 1.6k | 0 | 700 | 0.461 | 0 |
| Total | 833k | 86k | | 240 | 0 |
\*\* The location-based CO2e emissions of Llama 3.2 1B SpinQuant and Llama 3.2 3B SpinQuant are less than 0.001 metric tonnes each. This is due to the minimal training GPU hours that are required.
The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others.
## Training Data
**Overview:** Llama 3.2 was pretrained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pretraining stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO).
**Data Freshness:** The pretraining data has a cutoff of December 2023\.
## Quantization
### Quantization Scheme
We designed the current quantization scheme with the [PyTorch’s ExecuTorch](https://github.com/pytorch/executorch) inference framework and Arm CPU backend in mind, taking into account metrics including model quality, prefill/decoding speed, and memory footprint. Our quantization scheme involves three parts:
- All linear layers in all transformer blocks are quantized to a 4-bit groupwise scheme (with a group size of 32) for weights and 8-bit per-token dynamic quantization for activations.
- The classification layer is quantized to 8-bit per-channel for weight and 8-bit per token dynamic quantization for activation.
- Similar to classification layer, an 8-bit per channel quantization is used for embedding layer.
### Quantization-Aware Training and LoRA
The quantization-aware training (QAT) with low-rank adaptation (LoRA) models went through only post-training stages, using the same data as the full precision models. To initialize QAT, we utilize BF16 Llama 3.2 model checkpoints obtained after supervised fine-tuning (SFT) and perform an additional full round of SFT training with QAT. We then freeze the backbone of the QAT model and perform another round of SFT with LoRA adaptors applied to all layers within the transformer block. Meanwhile, the LoRA adaptors' weights and activations are maintained in BF16. Because our approach is similar to QLoRA of Dettmers et al., (2023) (i.e., quantization followed by LoRA adapters), we refer this method as QLoRA. Finally, we fine-tune the resulting model (both backbone and LoRA adaptors) using direct preference optimization (DPO).
### SpinQuant
[SpinQuant](https://arxiv.org/abs/2405.16406) was applied, together with generative post-training quantization (GPTQ). For the SpinQuant rotation matrix fine-tuning, we optimized for 100 iterations, using 800 samples with sequence-length 2048 from the WikiText 2 dataset. For GPTQ, we used 128 samples from the same dataset with the same sequence-length.
## Benchmarks \- English Text
In this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library.
### Base Pretrained Models
| Category | Benchmark | \# Shots | Metric | Llama 3.2 1B | Llama 3.2 3B | Llama 3.1 8B |
| ----- | ----- | :---: | :---: | :---: | :---: | :---: |
| General | MMLU | 5 | macro\_avg/acc\_char | 32.2 | 58 | 66.7 |
| | AGIEval English | 3-5 | average/acc\_char | 23.3 | 39.2 | 47.8 |
| | ARC-Challenge | 25 | acc\_char | 32.8 | 69.1 | 79.7 |
| Reading comprehension | SQuAD | 1 | em | 49.2 | 67.7 | 77 |
| | QuAC (F1) | 1 | f1 | 37.9 | 42.9 | 44.9 |
| | DROP (F1) | 3 | f1 | 28.0 | 45.2 | 59.5 |
| Long Context | Needle in Haystack | 0 | em | 96.8 | 1 | 1 |
### Instruction Tuned Models
| Capability | | Benchmark | \# Shots | Metric | Llama 3.2 1B bf16 | Llama 3.2 1B Vanilla PTQ\*\* | Llama 3.2 1B Spin Quant | Llama 3.2 1B QLoRA | Llama 3.2 3B bf16 | Llama 3.2 3B Vanilla PTQ\*\* | Llama 3.2 3B Spin Quant | Llama 3.2 3B QLoRA | Llama 3.1 8B |
| :---: | ----- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| General | | MMLU | 5 | macro\_avg/acc | 49.3 | 43.3 | 47.3 | 49.0 | 63.4 | 60.5 | 62 | 62.4 | 69.4 |
| Re-writing | | Open-rewrite eval | 0 | micro\_avg/rougeL | 41.6 | 39.2 | 40.9 | 41.2 | 40.1 | 40.3 | 40.8 | 40.7 | 40.9 |
| Summarization | | TLDR9+ (test) | 1 | rougeL | 16.8 | 14.9 | 16.7 | 16.8 | 19.0 | 19.1 | 19.2 | 19.1 | 17.2 |
| Instruction following | | IFEval | 0 | Avg(Prompt/Instruction acc Loose/Strict) | 59.5 | 51.5 | 58.4 | 55.6 | 77.4 | 73.9 | 73.5 | 75.9 | 80.4 |
| Math | | GSM8K (CoT) | 8 | em\_maj1@1 | 44.4 | 33.1 | 40.6 | 46.5 | 77.7 | 72.9 | 75.7 | 77.9 | 84.5 |
| | | MATH (CoT) | 0 | final\_em | 30.6 | 20.5 | 25.3 | 31.0 | 48.0 | 44.2 | 45.3 | 49.2 | 51.9 |
| Reasoning | | ARC-C | 0 | acc | 59.4 | 54.3 | 57 | 60.7 | 78.6 | 75.6 | 77.6 | 77.6 | 83.4 |
| | | GPQA | 0 | acc | 27.2 | 25.9 | 26.3 | 25.9 | 32.8 | 32.8 | 31.7 | 33.9 | 32.8 |
| | | Hellaswag | 0 | acc | 41.2 | 38.1 | 41.3 | 41.5 | 69.8 | 66.3 | 68 | 66.3 | 78.7 |
| Tool Use | | BFCL V2 | 0 | acc | 25.7 | 14.3 | 15.9 | 23.7 | 67.0 | 53.4 | 60.1 | 63.5 | 67.1 |
| | | Nexus | 0 | macro\_avg/acc | 13.5 | 5.2 | 9.6 | 12.5 | 34.3 | 32.4 | 31.5 | 30.1 | 38.5 |
| Long Context | | InfiniteBench/En.QA | 0 | longbook\_qa/f1 | 20.3 | N/A | N/A | N/A | 19.8 | N/A | N/A | N/A | 27.3 |
| | | InfiniteBench/En.MC | 0 | longbook\_choice/acc | 38.0 | N/A | N/A | N/A | 63.3 | N/A | N/A | N/A | 72.2 |
| | | NIH/Multi-needle | 0 | recall | 75.0 | N/A | N/A | N/A | 84.7 | N/A | N/A | N/A | 98.8 |
| Multilingual | | MGSM (CoT) | 0 | em | 24.5 | 13.7 | 18.2 | 24.4 | 58.2 | 48.9 | 54.3 | 56.8 | 68.9 |
\*\*for comparison purposes only. Model not released.
### Multilingual Benchmarks
| Category | Benchmark | Language | Llama 3.2 1B | Llama 3.2 1B Vanilla PTQ\*\* | Llama 3.2 1B Spin Quant | Llama 3.2 1B QLoRA | Llama 3.2 3B | Llama 3.2 3B Vanilla PTQ\*\* | Llama 3.2 3B Spin Quant | Llama 3.2 3B QLoRA | Llama 3.1 8B |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| General | MMLU (5-shot, macro_avg/acc) | Portuguese | 39.8 | 34.9 | 38.9 | 40.2 | 54.5 | 50.9 | 53.3 | 53.4 | 62.1 |
| | | Spanish | 41.5 | 36.0 | 39.8 | 41.8 | 55.1 | 51.9 | 53.6 | 53.6 | 62.5 |
| | | Italian | 39.8 | 34.9 | 38.1 | 40.6 | 53.8 | 49.9 | 52.1 | 51.7 | 61.6 |
| | | German | 39.2 | 34.9 | 37.5 | 39.6 | 53.3 | 50.0 | 52.2 | 51.3 | 60.6 |
| | | French | 40.5 | 34.8 | 39.2 | 40.8 | 54.6 | 51.2 | 53.3 | 53.3 | 62.3 |
| | | Hindi | 33.5 | 30.0 | 32.1 | 34.0 | 43.3 | 40.4 | 42.0 | 42.1 | 50.9 |
| | | Thai | 34.7 | 31.2 | 32.4 | 34.9 | 44.5 | 41.3 | 44.0 | 42.2 | 50.3 |
\*\*for comparison purposes only. Model not released.
## Inference time
In the below table, we compare the performance metrics of different quantization methods (SpinQuant and QAT \+ LoRA) with the BF16 baseline. The evaluation was done using the [ExecuTorch](https://github.com/pytorch/executorch) framework as the inference engine, with the ARM CPU as a backend using Android OnePlus 12 device.
| Category | Decode (tokens/sec) | Time-to-first-token (sec) | Prefill (tokens/sec) | Model size (PTE file size in MB) | Memory size (RSS in MB) |
| :---- | ----- | ----- | ----- | ----- | ----- |
| 1B BF16 (baseline) | 19.2 | 1.0 | 60.3 | 2358 | 3,185 |
| 1B SpinQuant | 50.2 (2.6x) | 0.3 (-76.9%) | 260.5 (4.3x) | 1083 (-54.1%) | 1,921 (-39.7%) |
| 1B QLoRA | 45.8 (2.4x) | 0.3 (-76.0%) | 252.0 (4.2x) | 1127 (-52.2%) | 2,255 (-29.2%) |
| 3B BF16 (baseline) | 7.6 | 3.0 | 21.2 | 6129 | 7,419 |
| 3B SpinQuant | 19.7 (2.6x) | 0.7 (-76.4%) | 89.7 (4.2x) | 2435 (-60.3%) | 3,726 (-49.8%) |
| 3B QLoRA | 18.5 (2.4x) | 0.7 (-76.1%) | 88.8 (4.2x) | 2529 (-58.7%) | 4,060 (-45.3%) |
(\*) The performance measurement is done using an adb binary-based approach.
(\*\*) It is measured on an Android OnePlus 12 device.
(\*\*\*) Time-to-first-token (TTFT) is measured with prompt length=64
*Footnote:*
- *Decode (tokens/second) is for how quickly it keeps generating. Higher is better.*
- *Time-to-first-token (TTFT for shorthand) is for how fast it generates the first token for a given prompt. Lower is better.*
- *Prefill is the inverse of TTFT (aka 1/TTFT) in tokens/second. Higher is better*
- *Model size \- how big is the model, measured by, PTE file, a binary file format for ExecuTorch*
- *RSS size \- Memory usage in resident set size (RSS)*
## Responsibility & Safety
As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks:
1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama
2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm
3. Provide protections for the community to help prevent the misuse of our models
### Responsible Deployment
**Approach:** Llama is a foundational technology designed to be used in a variety of use cases. Examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models, enabling the world to benefit from the technology power, by aligning our model safety for generic use cases and addressing a standard set of harms. Developers are then in the driver’s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/).
#### Llama 3.2 Instruct
**Objective:** Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/).
**Fine-Tuning Data:** We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control.
**Refusals and Tone:** Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines.
#### Llama 3.2 Systems
**Safety as a System:** Large language models, including Llama 3.2, **are not designed to be deployed in isolation** but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box.
### New Capabilities and Use Cases
**Technological Advancement:** Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well.
**Constrained Environments:** Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version.
### Evaluations
**Scaled Evaluations:** We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case.
**Red Teaming:** We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets.
### Critical Risks
In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas:
**1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons):** Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons and have determined that such testing also applies to the smaller 1B and 3B models.
**2\. Child Safety:** Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
**3\. Cyber Attacks:** For Llama 3.1 405B, our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed.
Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Because Llama 3.2’s 1B and 3B models are smaller and less capable models than Llama 3.1 405B, we broadly believe that the testing conducted for the 405B model also applies to Llama 3.2 models.
### Community
**Industry Partnerships:** Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
**Grants:** We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists).
**Reporting:** Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
**Values:** The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
**Testing:** Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
|
harshvardhanj733/results_german
|
harshvardhanj733
| 2024-11-10T17:49:34Z | 177 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-10T17:48:52Z |
---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: results_german
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results_german
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1187
- Accuracy: 0.6630
- Precision: 0.6685
- Recall: 0.6630
- F1: 0.6642
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 1.0 | 181 | 1.0659 | 0.4061 | 0.3156 | 0.4061 | 0.3260 |
| No log | 2.0 | 362 | 1.0165 | 0.4807 | 0.5137 | 0.4807 | 0.4334 |
| 1.0194 | 3.0 | 543 | 0.8416 | 0.6381 | 0.6474 | 0.6381 | 0.6335 |
| 1.0194 | 4.0 | 724 | 0.8182 | 0.6464 | 0.6469 | 0.6464 | 0.6434 |
| 1.0194 | 5.0 | 905 | 0.8988 | 0.6271 | 0.6610 | 0.6271 | 0.6135 |
| 0.7934 | 6.0 | 1086 | 0.8500 | 0.6657 | 0.6661 | 0.6657 | 0.6654 |
| 0.7934 | 7.0 | 1267 | 0.9742 | 0.6657 | 0.6722 | 0.6657 | 0.6675 |
| 0.7934 | 8.0 | 1448 | 1.0340 | 0.6630 | 0.6656 | 0.6630 | 0.6627 |
| 0.6352 | 9.0 | 1629 | 1.0133 | 0.6575 | 0.6601 | 0.6575 | 0.6586 |
| 0.6352 | 10.0 | 1810 | 0.9776 | 0.6630 | 0.6689 | 0.6630 | 0.6640 |
| 0.6352 | 11.0 | 1991 | 1.1097 | 0.6630 | 0.6681 | 0.6630 | 0.6640 |
| 0.5078 | 12.0 | 2172 | 1.1187 | 0.6630 | 0.6685 | 0.6630 | 0.6642 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1
|
LBK95/Llama-2-7b-hf-DPO-LookAhead-0_TTree1.4_TT0.9_TP0.7_TE0.2_V5
|
LBK95
| 2024-11-10T17:45:20Z | 13 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"dpo",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2024-11-10T11:55:38Z |
---
base_model: meta-llama/Llama-2-7b-hf
library_name: peft
license: llama2
tags:
- trl
- dpo
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-DPO-LookAhead-0_TTree1.4_TT0.9_TP0.7_TE0.2_V5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Llama-2-7b-hf-DPO-LookAhead-0_TTree1.4_TT0.9_TP0.7_TE0.2_V5
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0897
- Rewards/chosen: -2.9914
- Rewards/rejected: -2.7155
- Rewards/accuracies: 0.4000
- Rewards/margins: -0.2759
- Logps/rejected: -168.0010
- Logps/chosen: -174.0661
- Logits/rejected: -0.5254
- Logits/chosen: -0.5339
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.7826 | 0.2993 | 66 | 0.6590 | 0.0849 | 0.0090 | 0.8000 | 0.0759 | -140.7556 | -143.3033 | 0.0847 | 0.0794 |
| 0.639 | 0.5986 | 132 | 0.6196 | 0.1097 | -0.0511 | 0.9000 | 0.1607 | -141.3567 | -143.0557 | 0.0753 | 0.0696 |
| 0.5359 | 0.8980 | 198 | 0.6393 | 0.0423 | -0.0866 | 0.8000 | 0.1290 | -141.7119 | -143.7288 | 0.0629 | 0.0567 |
| 0.2727 | 1.1973 | 264 | 0.8080 | -1.1508 | -1.3039 | 0.6000 | 0.1532 | -153.8851 | -155.6598 | -0.0274 | -0.0343 |
| 0.3407 | 1.4966 | 330 | 0.6648 | -0.9615 | -1.1845 | 0.7000 | 0.2230 | -152.6907 | -153.7668 | -0.0764 | -0.0838 |
| 0.3991 | 1.7959 | 396 | 0.7534 | -1.2141 | -1.2811 | 0.6000 | 0.0670 | -153.6568 | -156.2932 | -0.1934 | -0.2005 |
| 0.1309 | 2.0952 | 462 | 0.8973 | -1.9586 | -1.8725 | 0.4000 | -0.0861 | -159.5707 | -163.7383 | -0.3197 | -0.3272 |
| 0.0603 | 2.3946 | 528 | 1.0892 | -2.8596 | -2.5458 | 0.3000 | -0.3138 | -166.3034 | -172.7478 | -0.4837 | -0.4920 |
| 0.1481 | 2.6939 | 594 | 1.1046 | -3.0656 | -2.7656 | 0.4000 | -0.2999 | -168.5022 | -174.8080 | -0.5326 | -0.5412 |
| 0.2564 | 2.9932 | 660 | 1.0897 | -2.9914 | -2.7155 | 0.4000 | -0.2759 | -168.0010 | -174.0661 | -0.5254 | -0.5339 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1
|
ihughes15234/phi35_tictactoe_dpo1epoch_v5
|
ihughes15234
| 2024-11-10T17:44:09Z | 80 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:ihughes15234/phi_3_5_mini_tictactoe1200",
"base_model:finetune:ihughes15234/phi_3_5_mini_tictactoe1200",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-10T17:32:57Z |
---
base_model: ihughes15234/phi_3_5_mini_tictactoe1200
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** ihughes15234
- **License:** apache-2.0
- **Finetuned from model :** ihughes15234/phi_3_5_mini_tictactoe1200
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
mradermacher/Mistral-7B-Discord-0.1-DPO-GGUF
|
mradermacher
| 2024-11-10T17:41:13Z | 56 | 0 |
transformers
|
[
"transformers",
"gguf",
"generated_from_trainer",
"en",
"base_model:BlouseJury/Mistral-7B-Discord-0.1-DPO",
"base_model:quantized:BlouseJury/Mistral-7B-Discord-0.1-DPO",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-11-06T16:16:44Z |
---
base_model: BlouseJury/Mistral-7B-Discord-0.1-DPO
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- generated_from_trainer
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/BlouseJury/Mistral-7B-Discord-0.1-DPO
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.Q4_0_4_4.gguf) | Q4_0_4_4 | 4.2 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.f16.gguf) | f16 | 14.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
bgahye/ddpm-celebahq-finetuned-butterflies-2epochs
|
bgahye
| 2024-11-10T17:28:20Z | 44 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2024-11-10T17:28:03Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
Describe your model here
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('bgahye/ddpm-celebahq-finetuned-butterflies-2epochs')
image = pipeline().images[0]
image
```
|
lafarizo/code_defect_detection_v1
|
lafarizo
| 2024-11-10T17:24:12Z | 110 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"code-defect-detection",
"c",
"dataset:semeru/code-code-DefectDetection",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-10T15:16:50Z |
---
title: "Code Defect Detection v1"
tags:
- code-defect-detection
- c
library_name: "transformers"
datasets:
- semeru/code-code-DefectDetection
---
# Code Defect Detection v1
Code Defect Detection for C language
### Model Sources
- **Repository:** [mrm8488/codebert2codebert-finetuned-code-defect-detection](https://huggingface.co/mrm8488/codebert2codebert-finetuned-code-defect-detection)
### Dataset
- **Repository:** [semeru/code-code-DefectDetection](https://huggingface.co/datasets/semeru/code-code-DefectDetection)
| Results | Value |
|---------------------------|--------------|
| **Evaluation Loss** | 0.7605 |
| **Accuracy** | 66.76% |
| **Precision** | 65.64% |
| **Recall** | 58.01% |
| **F1 Score** | 61.59% |
| **AUC** | 73.52% |
|
mradermacher/PlatYi-34B-Q-i1-GGUF
|
mradermacher
| 2024-11-10T17:22:12Z | 14 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"dataset:garage-bAInd/Open-Platypus",
"base_model:kyujinpy/PlatYi-34B-Q",
"base_model:quantized:kyujinpy/PlatYi-34B-Q",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2024-11-10T11:49:59Z |
---
base_model: kyujinpy/PlatYi-34B-Q
datasets:
- garage-bAInd/Open-Platypus
language:
- en
library_name: transformers
license: cc-by-nc-sa-4.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/kyujinpy/PlatYi-34B-Q
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/PlatYi-34B-Q-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/PlatYi-34B-Q-i1-GGUF/resolve/main/PlatYi-34B-Q.i1-IQ1_S.gguf) | i1-IQ1_S | 7.6 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/PlatYi-34B-Q-i1-GGUF/resolve/main/PlatYi-34B-Q.i1-IQ1_M.gguf) | i1-IQ1_M | 8.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/PlatYi-34B-Q-i1-GGUF/resolve/main/PlatYi-34B-Q.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.4 | |
| [GGUF](https://huggingface.co/mradermacher/PlatYi-34B-Q-i1-GGUF/resolve/main/PlatYi-34B-Q.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.4 | |
| [GGUF](https://huggingface.co/mradermacher/PlatYi-34B-Q-i1-GGUF/resolve/main/PlatYi-34B-Q.i1-IQ2_S.gguf) | i1-IQ2_S | 11.0 | |
| [GGUF](https://huggingface.co/mradermacher/PlatYi-34B-Q-i1-GGUF/resolve/main/PlatYi-34B-Q.i1-IQ2_M.gguf) | i1-IQ2_M | 11.9 | |
| [GGUF](https://huggingface.co/mradermacher/PlatYi-34B-Q-i1-GGUF/resolve/main/PlatYi-34B-Q.i1-Q2_K.gguf) | i1-Q2_K | 12.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/PlatYi-34B-Q-i1-GGUF/resolve/main/PlatYi-34B-Q.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 13.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/PlatYi-34B-Q-i1-GGUF/resolve/main/PlatYi-34B-Q.i1-IQ3_XS.gguf) | i1-IQ3_XS | 14.3 | |
| [GGUF](https://huggingface.co/mradermacher/PlatYi-34B-Q-i1-GGUF/resolve/main/PlatYi-34B-Q.i1-Q3_K_S.gguf) | i1-Q3_K_S | 15.1 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/PlatYi-34B-Q-i1-GGUF/resolve/main/PlatYi-34B-Q.i1-IQ3_S.gguf) | i1-IQ3_S | 15.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/PlatYi-34B-Q-i1-GGUF/resolve/main/PlatYi-34B-Q.i1-IQ3_M.gguf) | i1-IQ3_M | 15.7 | |
| [GGUF](https://huggingface.co/mradermacher/PlatYi-34B-Q-i1-GGUF/resolve/main/PlatYi-34B-Q.i1-Q3_K_M.gguf) | i1-Q3_K_M | 16.8 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/PlatYi-34B-Q-i1-GGUF/resolve/main/PlatYi-34B-Q.i1-Q3_K_L.gguf) | i1-Q3_K_L | 18.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/PlatYi-34B-Q-i1-GGUF/resolve/main/PlatYi-34B-Q.i1-IQ4_XS.gguf) | i1-IQ4_XS | 18.6 | |
| [GGUF](https://huggingface.co/mradermacher/PlatYi-34B-Q-i1-GGUF/resolve/main/PlatYi-34B-Q.i1-Q4_0.gguf) | i1-Q4_0 | 19.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/PlatYi-34B-Q-i1-GGUF/resolve/main/PlatYi-34B-Q.i1-Q4_K_S.gguf) | i1-Q4_K_S | 19.7 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/PlatYi-34B-Q-i1-GGUF/resolve/main/PlatYi-34B-Q.i1-Q4_K_M.gguf) | i1-Q4_K_M | 20.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PlatYi-34B-Q-i1-GGUF/resolve/main/PlatYi-34B-Q.i1-Q5_K_S.gguf) | i1-Q5_K_S | 23.8 | |
| [GGUF](https://huggingface.co/mradermacher/PlatYi-34B-Q-i1-GGUF/resolve/main/PlatYi-34B-Q.i1-Q5_K_M.gguf) | i1-Q5_K_M | 24.4 | |
| [GGUF](https://huggingface.co/mradermacher/PlatYi-34B-Q-i1-GGUF/resolve/main/PlatYi-34B-Q.i1-Q6_K.gguf) | i1-Q6_K | 28.3 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mradermacher/PlatYi-34B-Q-GGUF
|
mradermacher
| 2024-11-10T17:22:10Z | 46 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"dataset:garage-bAInd/Open-Platypus",
"base_model:kyujinpy/PlatYi-34B-Q",
"base_model:quantized:kyujinpy/PlatYi-34B-Q",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-11-07T08:09:21Z |
---
base_model: kyujinpy/PlatYi-34B-Q
datasets:
- garage-bAInd/Open-Platypus
language:
- en
library_name: transformers
license: cc-by-nc-sa-4.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/kyujinpy/PlatYi-34B-Q
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/PlatYi-34B-Q-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/PlatYi-34B-Q-GGUF/resolve/main/PlatYi-34B-Q.Q2_K.gguf) | Q2_K | 12.9 | |
| [GGUF](https://huggingface.co/mradermacher/PlatYi-34B-Q-GGUF/resolve/main/PlatYi-34B-Q.Q3_K_S.gguf) | Q3_K_S | 15.1 | |
| [GGUF](https://huggingface.co/mradermacher/PlatYi-34B-Q-GGUF/resolve/main/PlatYi-34B-Q.Q3_K_M.gguf) | Q3_K_M | 16.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/PlatYi-34B-Q-GGUF/resolve/main/PlatYi-34B-Q.Q3_K_L.gguf) | Q3_K_L | 18.2 | |
| [GGUF](https://huggingface.co/mradermacher/PlatYi-34B-Q-GGUF/resolve/main/PlatYi-34B-Q.IQ4_XS.gguf) | IQ4_XS | 18.7 | |
| [GGUF](https://huggingface.co/mradermacher/PlatYi-34B-Q-GGUF/resolve/main/PlatYi-34B-Q.Q4_K_S.gguf) | Q4_K_S | 19.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PlatYi-34B-Q-GGUF/resolve/main/PlatYi-34B-Q.Q4_K_M.gguf) | Q4_K_M | 20.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PlatYi-34B-Q-GGUF/resolve/main/PlatYi-34B-Q.Q5_K_S.gguf) | Q5_K_S | 23.8 | |
| [GGUF](https://huggingface.co/mradermacher/PlatYi-34B-Q-GGUF/resolve/main/PlatYi-34B-Q.Q5_K_M.gguf) | Q5_K_M | 24.4 | |
| [GGUF](https://huggingface.co/mradermacher/PlatYi-34B-Q-GGUF/resolve/main/PlatYi-34B-Q.Q6_K.gguf) | Q6_K | 28.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/PlatYi-34B-Q-GGUF/resolve/main/PlatYi-34B-Q.Q8_0.gguf) | Q8_0 | 36.6 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
amirlandau/ByteCodeLLM_gemma_2_2b_v2
|
amirlandau
| 2024-11-10T17:20:11Z | 11 | 0 |
transformers
|
[
"transformers",
"gguf",
"gemma2",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/gemma-2-2b-bnb-4bit",
"base_model:quantized:unsloth/gemma-2-2b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-11-10T17:13:13Z |
---
base_model: unsloth/gemma-2-2b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma2
- gguf
---
# Uploaded model
- **Developed by:** amirlandau
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2-2b-bnb-4bit
This gemma2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
sunbv56/T5_Chatbot_CustomerSupport
|
sunbv56
| 2024-11-10T17:19:22Z | 354 | 1 | null |
[
"safetensors",
"t5",
"text2text-generation",
"en",
"dataset:bitext/Bitext-customer-support-llm-chatbot-training-dataset",
"base_model:google-t5/t5-base",
"base_model:finetune:google-t5/t5-base",
"license:apache-2.0",
"region:us"
] |
text2text-generation
| 2024-11-07T08:04:05Z |
---
license: apache-2.0
language:
- en
pipeline_tag: text2text-generation
datasets:
- bitext/Bitext-customer-support-llm-chatbot-training-dataset
base_model:
- google-t5/t5-base
---
## About model
A customer support chatbot built on Google's T5 architecture and fine-tuned using the bitext/Bitext-customer-support-llm-chatbot-training-dataset. Designed to understand natural language and provide accurate, efficient responses for a wide range of customer service scenarios. Ideal for automating support, answering queries, and enhancing user experience in customer-facing applications.
## How to Get Started with the Model
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
model_name = "sunbv56/T5_Chatbot_CustomerSupport"
tokenizer = T5Tokenizer.from_pretrained(model_name, legacy=False)
model = T5ForConditionalGeneration.from_pretrained(model_name)
```
## Example code here
https://www.kaggle.com/code/thuntrngbnh/test-t5-chatbot-customersupport
|
Siheng99/Qwen2.5-14B-Instruct-SEALONG
|
Siheng99
| 2024-11-10T17:15:47Z | 5 | 1 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-10T17:12:01Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
thinhlpg/vi-gemma-2b-RAG-Q4_K_S-GGUF
|
thinhlpg
| 2024-11-10T17:10:56Z | 16 | 1 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"retrieval-augmented-generation",
"unsloth",
"gemma",
"trl",
"sft",
"llama-cpp",
"gguf-my-repo",
"en",
"vi",
"base_model:ricepaper/vi-gemma-2b-RAG",
"base_model:quantized:ricepaper/vi-gemma-2b-RAG",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-10T17:10:42Z |
---
base_model: ricepaper/vi-gemma-2b-RAG
language:
- en
- vi
license: apache-2.0
tags:
- text-generation-inference
- retrieval-augmented-generation
- transformers
- unsloth
- gemma
- trl
- sft
- llama-cpp
- gguf-my-repo
---
# thinhlpg/vi-gemma-2b-RAG-Q4_K_S-GGUF
This model was converted to GGUF format from [`ricepaper/vi-gemma-2b-RAG`](https://huggingface.co/ricepaper/vi-gemma-2b-RAG) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/ricepaper/vi-gemma-2b-RAG) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo thinhlpg/vi-gemma-2b-RAG-Q4_K_S-GGUF --hf-file vi-gemma-2b-rag-q4_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo thinhlpg/vi-gemma-2b-RAG-Q4_K_S-GGUF --hf-file vi-gemma-2b-rag-q4_k_s.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo thinhlpg/vi-gemma-2b-RAG-Q4_K_S-GGUF --hf-file vi-gemma-2b-rag-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo thinhlpg/vi-gemma-2b-RAG-Q4_K_S-GGUF --hf-file vi-gemma-2b-rag-q4_k_s.gguf -c 2048
```
|
vanwdai/vandai-structure-recognition-3
|
vanwdai
| 2024-11-10T17:07:22Z | 164 | 0 |
transformers
|
[
"transformers",
"safetensors",
"table-transformer",
"object-detection",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2024-11-10T17:07:15Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mav23/Yi-34B-200K-GGUF
|
mav23
| 2024-11-10T17:06:08Z | 195 | 0 | null |
[
"gguf",
"text-generation",
"arxiv:2403.04652",
"arxiv:2311.16502",
"arxiv:2401.11944",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-10T12:04:14Z |
---
license: apache-2.0
widget:
- example_title: "Yi-34B-Chat"
text: "hi"
output:
text: " Hello! How can I assist you today?"
- example_title: "Yi-34B"
text: "There's a place where time stands still. A place of breath taking wonder, but also"
output:
text: " an eerie sense that something is just not right…\nBetween the two worlds lies The Forgotten Kingdom - home to creatures long since thought extinct and ancient magic so strong it defies belief! Only here can you find what has been lost for centuries: An Elixir Of Life which will restore youth and vitality if only those who seek its power are brave enough to face up against all manner of dangers lurking in this mysterious land! But beware; some say there may even exist powerful entities beyond our comprehension whose intentions towards humanity remain unclear at best ---- they might want nothing more than destruction itself rather then anything else from their quest after immortality (and maybe someone should tell them about modern medicine)? In any event though – one thing remains true regardless : whether or not success comes easy depends entirely upon how much effort we put into conquering whatever challenges lie ahead along with having faith deep down inside ourselves too ;) So let’s get started now shall We?"
pipeline_tag: text-generation
---
<div align="center">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_dark.svg" width="200px">
<source media="(prefers-color-scheme: light)" srcset="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="200px">
<img alt="specify theme context for images" src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg">
</picture>
</br>
</br>
<div style="display: inline-block;">
<a href="https://github.com/01-ai/Yi/actions/workflows/build_docker_image.yml">
<img src="https://github.com/01-ai/Yi/actions/workflows/build_docker_image.yml/badge.svg">
</a>
</div>
<div style="display: inline-block;">
<a href="mailto:[email protected]">
<img src="https://img.shields.io/badge/✉️[email protected]">
</a>
</div>
</div>
<div align="center">
<h3 align="center">Building the Next Generation of Open-Source and Bilingual LLMs</h3>
</div>
<p align="center">
🤗 <a href="https://huggingface.co/01-ai" target="_blank">Hugging Face</a> • 🤖 <a href="https://www.modelscope.cn/organization/01ai/" target="_blank">ModelScope</a> • ✡️ <a href="https://wisemodel.cn/organization/01.AI" target="_blank">WiseModel</a>
</p>
<p align="center">
👩🚀 Ask questions or discuss ideas on <a href="https://github.com/01-ai/Yi/discussions" target="_blank"> GitHub </a>
</p>
<p align="center">
👋 Join us on <a href="https://discord.gg/hYUwWddeAu" target="_blank"> 👾 Discord </a> or <a href="有官方的微信群嘛 · Issue #43 · 01-ai/Yi" target="_blank"> 💬 WeChat </a>
</p>
<p align="center">
📝 Check out <a href="https://arxiv.org/abs/2403.04652"> Yi Tech Report </a>
</p>
<p align="center">
📚 Grow at <a href="#learning-hub"> Yi Learning Hub </a>
</p>
<!-- DO NOT REMOVE ME -->
<hr>
<details open>
<summary></b>📕 Table of Contents</b></summary>
- [What is Yi?](#what-is-yi)
- [Introduction](#introduction)
- [Models](#models)
- [Chat models](#chat-models)
- [Base models](#base-models)
- [Model info](#model-info)
- [News](#news)
- [How to use Yi?](#how-to-use-yi)
- [Quick start](#quick-start)
- [Choose your path](#choose-your-path)
- [pip](#quick-start---pip)
- [docker](#quick-start---docker)
- [llama.cpp](#quick-start---llamacpp)
- [conda-lock](#quick-start---conda-lock)
- [Web demo](#web-demo)
- [Fine-tuning](#fine-tuning)
- [Quantization](#quantization)
- [Deployment](#deployment)
- [FAQ](#faq)
- [Learning hub](#learning-hub)
- [Why Yi?](#why-yi)
- [Ecosystem](#ecosystem)
- [Upstream](#upstream)
- [Downstream](#downstream)
- [Serving](#serving)
- [Quantization](#quantization-1)
- [Fine-tuning](#fine-tuning-1)
- [API](#api)
- [Benchmarks](#benchmarks)
- [Base model performance](#base-model-performance)
- [Chat model performance](#chat-model-performance)
- [Tech report](#tech-report)
- [Citation](#citation)
- [Who can use Yi?](#who-can-use-yi)
- [Misc.](#misc)
- [Acknowledgements](#acknowledgments)
- [Disclaimer](#disclaimer)
- [License](#license)
</details>
<hr>
# What is Yi?
## Introduction
- 🤖 The Yi series models are the next generation of open-source large language models trained from scratch by [01.AI](https://01.ai/).
- 🙌 Targeted as a bilingual language model and trained on 3T multilingual corpus, the Yi series models become one of the strongest LLM worldwide, showing promise in language understanding, commonsense reasoning, reading comprehension, and more. For example,
- Yi-34B-Chat model **landed in second place (following GPT-4 Turbo)**, outperforming other LLMs (such as GPT-4, Mixtral, Claude) on the AlpacaEval Leaderboard (based on data available up to January 2024).
- Yi-34B model **ranked first among all existing open-source models** (such as Falcon-180B, Llama-70B, Claude) in **both English and Chinese** on various benchmarks, including Hugging Face Open LLM Leaderboard (pre-trained) and C-Eval (based on data available up to November 2023).
- 🙏 (Credits to Llama) Thanks to the Transformer and Llama open-source communities, as they reduce the efforts required to build from scratch and enable the utilization of the same tools within the AI ecosystem.
<details style="display: inline;"><summary> If you're interested in Yi's adoption of Llama architecture and license usage policy, see <span style="color: green;">Yi's relation with Llama.</span> ⬇️</summary> <ul> <br>
> 💡 TL;DR
>
> The Yi series models adopt the same model architecture as Llama but are **NOT** derivatives of Llama.
- Both Yi and Llama are based on the Transformer structure, which has been the standard architecture for large language models since 2018.
- Grounded in the Transformer architecture, Llama has become a new cornerstone for the majority of state-of-the-art open-source models due to its excellent stability, reliable convergence, and robust compatibility. This positions Llama as the recognized foundational framework for models including Yi.
- Thanks to the Transformer and Llama architectures, other models can leverage their power, reducing the effort required to build from scratch and enabling the utilization of the same tools within their ecosystems.
- However, the Yi series models are NOT derivatives of Llama, as they do not use Llama's weights.
- As Llama's structure is employed by the majority of open-source models, the key factors of determining model performance are training datasets, training pipelines, and training infrastructure.
- Developing in a unique and proprietary way, Yi has independently created its own high-quality training datasets, efficient training pipelines, and robust training infrastructure entirely from the ground up. This effort has led to excellent performance with Yi series models ranking just behind GPT4 and surpassing Llama on the [Alpaca Leaderboard in Dec 2023](https://tatsu-lab.github.io/alpaca_eval/).
</ul>
</details>
<p align="right"> [
<a href="#top">Back to top ⬆️ </a> ]
</p>
## News
<details>
<summary>🔥 <b>2024-07-29</b>: The <a href="https://github.com/Haijian06/Yi/tree/main/Cookbook">Yi Cookbook 1.0 </a> is released, featuring tutorials and examples in both Chinese and English.</summary>
</details>
<details>
<summary>🎯 <b>2024-05-13</b>: The <a href="https://github.com/01-ai/Yi-1.5">Yi-1.5 series models </a> are open-sourced, further improving coding, math, reasoning, and instruction-following abilities.</summary>
</details>
<details>
<summary>🎯 <b>2024-03-16</b>: The <code>Yi-9B-200K</code> is open-sourced and available to the public.</summary>
</details>
<details>
<summary>🎯 <b>2024-03-08</b>: <a href="https://arxiv.org/abs/2403.04652">Yi Tech Report</a> is published! </summary>
</details>
<details open>
<summary>🔔 <b>2024-03-07</b>: The long text capability of the Yi-34B-200K has been enhanced. </summary>
<br>
In the "Needle-in-a-Haystack" test, the Yi-34B-200K's performance is improved by 10.5%, rising from 89.3% to an impressive 99.8%. We continue to pre-train the model on 5B tokens long-context data mixture and demonstrate a near-all-green performance.
</details>
<details open>
<summary>🎯 <b>2024-03-06</b>: The <code>Yi-9B</code> is open-sourced and available to the public.</summary>
<br>
<code>Yi-9B</code> stands out as the top performer among a range of similar-sized open-source models (including Mistral-7B, SOLAR-10.7B, Gemma-7B, DeepSeek-Coder-7B-Base-v1.5 and more), particularly excelling in code, math, common-sense reasoning, and reading comprehension.
</details>
<details open>
<summary>🎯 <b>2024-01-23</b>: The Yi-VL models, <code><a href="https://huggingface.co/01-ai/Yi-VL-34B">Yi-VL-34B</a></code> and <code><a href="https://huggingface.co/01-ai/Yi-VL-6B">Yi-VL-6B</a></code>, are open-sourced and available to the public.</summary>
<br>
<code><a href="https://huggingface.co/01-ai/Yi-VL-34B">Yi-VL-34B</a></code> has ranked <strong>first</strong> among all existing open-source models in the latest benchmarks, including <a href="https://arxiv.org/abs/2311.16502">MMMU</a> and <a href="https://arxiv.org/abs/2401.11944">CMMMU</a> (based on data available up to January 2024).</li>
</details>
<details>
<summary>🎯 <b>2023-11-23</b>: <a href="#chat-models">Chat models</a> are open-sourced and available to the public.</summary>
<br>This release contains two chat models based on previously released base models, two 8-bit models quantized by GPTQ, and two 4-bit models quantized by AWQ.
- `Yi-34B-Chat`
- `Yi-34B-Chat-4bits`
- `Yi-34B-Chat-8bits`
- `Yi-6B-Chat`
- `Yi-6B-Chat-4bits`
- `Yi-6B-Chat-8bits`
You can try some of them interactively at:
- [Hugging Face](https://huggingface.co/spaces/01-ai/Yi-34B-Chat)
- [Replicate](https://replicate.com/01-ai)
</details>
<details>
<summary>🔔 <b>2023-11-23</b>: The Yi Series Models Community License Agreement is updated to <a href="https://github.com/01-ai/Yi/blob/main/MODEL_LICENSE_AGREEMENT.txt">v2.1</a>.</summary>
</details>
<details>
<summary>🔥 <b>2023-11-08</b>: Invited test of Yi-34B chat model.</summary>
<br>Application form:
- [English](https://cn.mikecrm.com/l91ODJf)
- [Chinese](https://cn.mikecrm.com/gnEZjiQ)
</details>
<details>
<summary>🎯 <b>2023-11-05</b>: <a href="#base-models">The base models, </a><code>Yi-6B-200K</code> and <code>Yi-34B-200K</code>, are open-sourced and available to the public.</summary>
<br>This release contains two base models with the same parameter sizes as the previous
release, except that the context window is extended to 200K.
</details>
<details>
<summary>🎯 <b>2023-11-02</b>: <a href="#base-models">The base models, </a><code>Yi-6B</code> and <code>Yi-34B</code>, are open-sourced and available to the public.</summary>
<br>The first public release contains two bilingual (English/Chinese) base models
with the parameter sizes of 6B and 34B. Both of them are trained with 4K
sequence length and can be extended to 32K during inference time.
</details>
<p align="right"> [
<a href="#top">Back to top ⬆️ </a> ]
</p>
## Models
Yi models come in multiple sizes and cater to different use cases. You can also fine-tune Yi models to meet your specific requirements.
If you want to deploy Yi models, make sure you meet the [software and hardware requirements](#deployment).
### Chat models
| Model | Download |
|---|---|
|Yi-34B-Chat | • [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-34B-Chat) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-34B-Chat/summary) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-34B-Chat) |
|Yi-34B-Chat-4bits | • [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-34B-Chat-4bits) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-34B-Chat-4bits/summary) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-34B-Chat-4bits) |
|Yi-34B-Chat-8bits | • [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-34B-Chat-8bits) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-34B-Chat-8bits/summary) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-34B-Chat-8bits) |
|Yi-6B-Chat| • [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-6B-Chat) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-6B-Chat/summary) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-6B-Chat) |
|Yi-6B-Chat-4bits | • [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-6B-Chat-4bits) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-6B-Chat-4bits/summary) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-6B-Chat-4bits) |
|Yi-6B-Chat-8bits | • [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-6B-Chat-8bits) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-6B-Chat-8bits/summary) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-6B-Chat-8bits) |
<sub><sup> - 4-bit series models are quantized by AWQ. <br> - 8-bit series models are quantized by GPTQ <br> - All quantized models have a low barrier to use since they can be deployed on consumer-grade GPUs (e.g., 3090, 4090). </sup></sub>
### Base models
| Model | Download |
|---|---|
|Yi-34B| • [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-34B) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-34B/summary) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-6B-Chat-8bits) |
|Yi-34B-200K|• [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-34B-200K) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-34B-200K/summary) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-6B-Chat-8bits)|
|Yi-9B|• [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-9B) • [🤖 ModelScope](https://wisemodel.cn/models/01.AI/Yi-6B-Chat-8bits) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-9B)|
|Yi-9B-200K | • [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-9B-200K) • [🤖 ModelScope](https://wisemodel.cn/models/01.AI/Yi-9B-200K) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-6B-Chat-8bits) |
|Yi-6B| • [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-6B) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-6B/summary) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-6B-Chat-8bits) |
|Yi-6B-200K | • [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-6B-200K) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-6B-200K/summary) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-6B-Chat-8bits) |
<sub><sup> - 200k is roughly equivalent to 400,000 Chinese characters. <br> - If you want to use the previous version of the Yi-34B-200K (released on Nov 5, 2023), run `git checkout 069cd341d60f4ce4b07ec394e82b79e94f656cf` to download the weight. </sup></sub>
### Model info
- For chat and base models
<table>
<thead>
<tr>
<th>Model</th>
<th>Intro</th>
<th>Default context window</th>
<th>Pretrained tokens</th>
<th>Training Data Date</th>
</tr>
</thead>
<tbody><tr>
<td>6B series models</td>
<td>They are suitable for personal and academic use.</td>
<td rowspan="3">4K</td>
<td>3T</td>
<td rowspan="3">Up to June 2023</td>
</tr>
<tr>
<td>9B series models</td>
<td>It is the best at coding and math in the Yi series models.</td>
<td>Yi-9B is continuously trained based on Yi-6B, using 0.8T tokens.</td>
</tr>
<tr>
<td>34B series models</td>
<td>They are suitable for personal, academic, and commercial (particularly for small and medium-sized enterprises) purposes. It's a cost-effective solution that's affordable and equipped with emergent ability.</td>
<td>3T</td>
</tr>
</tbody></table>
- For chat models
<details style="display: inline;"><summary>For chat model limitations, see the explanations below. ⬇️</summary>
<ul>
<br>The released chat model has undergone exclusive training using Supervised Fine-Tuning (SFT). Compared to other standard chat models, our model produces more diverse responses, making it suitable for various downstream tasks, such as creative scenarios. Furthermore, this diversity is expected to enhance the likelihood of generating higher quality responses, which will be advantageous for subsequent Reinforcement Learning (RL) training.
<br>However, this higher diversity might amplify certain existing issues, including:
<li>Hallucination: This refers to the model generating factually incorrect or nonsensical information. With the model's responses being more varied, there's a higher chance of hallucination that are not based on accurate data or logical reasoning.</li>
<li>Non-determinism in re-generation: When attempting to regenerate or sample responses, inconsistencies in the outcomes may occur. The increased diversity can lead to varying results even under similar input conditions.</li>
<li>Cumulative Error: This occurs when errors in the model's responses compound over time. As the model generates more diverse responses, the likelihood of small inaccuracies building up into larger errors increases, especially in complex tasks like extended reasoning, mathematical problem-solving, etc.</li>
<li>To achieve more coherent and consistent responses, it is advisable to adjust generation configuration parameters such as temperature, top_p, or top_k. These adjustments can help in the balance between creativity and coherence in the model's outputs.</li>
</ul>
</details>
<p align="right"> [
<a href="#top">Back to top ⬆️ </a> ]
</p>
# How to use Yi?
- [Quick start](#quick-start)
- [Choose your path](#choose-your-path)
- [pip](#quick-start---pip)
- [docker](#quick-start---docker)
- [conda-lock](#quick-start---conda-lock)
- [llama.cpp](#quick-start---llamacpp)
- [Web demo](#web-demo)
- [Fine-tuning](#fine-tuning)
- [Quantization](#quantization)
- [Deployment](#deployment)
- [FAQ](#faq)
- [Learning hub](#learning-hub)
## Quick start
Getting up and running with Yi models is simple with multiple choices available. If you want more inference refer to the [Cookbook](https://github.com/01-ai/Yi/tree/main/Cookbook)
### Choose your path
Select one of the following paths to begin your journey with Yi!
#### 🎯 Deploy Yi locally
If you prefer to deploy Yi models locally,
- 🙋♀️ and you have **sufficient** resources (for example, NVIDIA A800 80GB), you can choose one of the following methods:
- [pip](#quick-start---pip)
- [Docker](#quick-start---docker)
- [conda-lock](#quick-start---conda-lock)
- 🙋♀️ and you have **limited** resources (for example, a MacBook Pro), you can use [llama.cpp](#quick-start---llamacpp).
#### 🎯 Not to deploy Yi locally
If you prefer not to deploy Yi models locally, you can explore Yi's capabilities using any of the following options.
##### 🙋♀️ Run Yi with APIs
If you want to explore more features of Yi, you can adopt one of these methods:
- Yi APIs (Yi official)
- [Early access has been granted](https://x.com/01AI_Yi/status/1735728934560600536?s=20) to some applicants. Stay tuned for the next round of access!
- [Yi APIs](https://replicate.com/01-ai/yi-34b-chat/api?tab=nodejs) (Replicate)
##### 🙋♀️ Run Yi in playground
If you want to chat with Yi with more customizable options (e.g., system prompt, temperature, repetition penalty, etc.), you can try one of the following options:
- [Yi-34B-Chat-Playground](https://platform.lingyiwanwu.com/prompt/playground) (Yi official)
- Access is available through a whitelist. Welcome to apply (fill out a form in [English](https://cn.mikecrm.com/l91ODJf) or [Chinese](https://cn.mikecrm.com/gnEZjiQ)).
- [Yi-34B-Chat-Playground](https://replicate.com/01-ai/yi-34b-chat) (Replicate)
##### 🙋♀️ Chat with Yi
If you want to chat with Yi, you can use one of these online services, which offer a similar user experience:
- [Yi-34B-Chat](https://huggingface.co/spaces/01-ai/Yi-34B-Chat) (Yi official on Hugging Face)
- No registration is required.
- [Yi-34B-Chat](https://platform.lingyiwanwu.com/) (Yi official beta)
- Access is available through a whitelist. Welcome to apply (fill out a form in [English](https://cn.mikecrm.com/l91ODJf) or [Chinese](https://cn.mikecrm.com/gnEZjiQ)).
<p align="right"> [
<a href="#top">Back to top ⬆️ </a> ]
</p>
### Quick start - pip
This tutorial guides you through every step of running **Yi-34B-Chat locally on an A800 (80G)** and then performing inference.
#### Step 0: Prerequisites
- Make sure Python 3.10 or a later version is installed.
- If you want to run other Yi models, see [software and hardware requirements](#deployment).
#### Step 1: Prepare your environment
To set up the environment and install the required packages, execute the following command.
```bash
git clone https://github.com/01-ai/Yi.git
cd yi
pip install -r requirements.txt
```
#### Step 2: Download the Yi model
You can download the weights and tokenizer of Yi models from the following sources:
- [Hugging Face](https://huggingface.co/01-ai)
- [ModelScope](https://www.modelscope.cn/organization/01ai/)
- [WiseModel](https://wisemodel.cn/organization/01.AI)
#### Step 3: Perform inference
You can perform inference with Yi chat or base models as below.
##### Perform inference with Yi chat model
1. Create a file named `quick_start.py` and copy the following content to it.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = '<your-model-path>'
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
# Since transformers 4.35.0, the GPT-Q/AWQ model can be loaded using AutoModelForCausalLM.
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
```
2. Run `quick_start.py`.
```bash
python quick_start.py
```
Then you can see an output similar to the one below. 🥳
```bash
Hello! How can I assist you today?
```
##### Perform inference with Yi base model
- Yi-34B
The steps are similar to [pip - Perform inference with Yi chat model](#perform-inference-with-yi-chat-model).
You can use the existing file [`text_generation.py`](https://github.com/01-ai/Yi/tree/main/demo).
```bash
python demo/text_generation.py --model <your-model-path>
```
Then you can see an output similar to the one below. 🥳
<details>
<summary>Output. ⬇️ </summary>
<br>
**Prompt**: Let me tell you an interesting story about cat Tom and mouse Jerry,
**Generation**: Let me tell you an interesting story about cat Tom and mouse Jerry, which happened in my childhood. My father had a big house with two cats living inside it to kill mice. One day when I was playing at home alone, I found one of the tomcats lying on his back near our kitchen door, looking very much like he wanted something from us but couldn’t get up because there were too many people around him! He kept trying for several minutes before finally giving up...
</details>
- Yi-9B
Input
```bash
from transformers import AutoModelForCausalLM, AutoTokenizer
MODEL_DIR = "01-ai/Yi-9B"
model = AutoModelForCausalLM.from_pretrained(MODEL_DIR, torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR, use_fast=False)
input_text = "# write the quick sort algorithm"
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
Output
```bash
# write the quick sort algorithm
def quick_sort(arr):
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quick_sort(left) + middle + quick_sort(right)
# test the quick sort algorithm
print(quick_sort([3, 6, 8, 10, 1, 2, 1]))
```
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</p>
### Quick start - Docker
<details>
<summary> Run Yi-34B-chat locally with Docker: a step-by-step guide. ⬇️</summary>
<br>This tutorial guides you through every step of running <strong>Yi-34B-Chat on an A800 GPU</strong> or <strong>4*4090</strong> locally and then performing inference.
<h4>Step 0: Prerequisites</h4>
<p>Make sure you've installed <a href="https://docs.docker.com/engine/install/?open_in_browser=true">Docker</a> and <a href="https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html">nvidia-container-toolkit</a>.</p>
<h4> Step 1: Start Docker </h4>
<pre><code>docker run -it --gpus all \
-v <your-model-path>: /models
ghcr.io/01-ai/yi:latest
</code></pre>
<p>Alternatively, you can pull the Yi Docker image from <code>registry.lingyiwanwu.com/ci/01-ai/yi:latest</code>.</p>
<h4>Step 2: Perform inference</h4>
<p>You can perform inference with Yi chat or base models as below.</p>
<h5>Perform inference with Yi chat model</h5>
<p>The steps are similar to <a href="#perform-inference-with-yi-chat-model">pip - Perform inference with Yi chat model</a>.</p>
<p><strong>Note</strong> that the only difference is to set <code>model_path = '<your-model-mount-path>'</code> instead of <code>model_path = '<your-model-path>'</code>.</p>
<h5>Perform inference with Yi base model</h5>
<p>The steps are similar to <a href="#perform-inference-with-yi-base-model">pip - Perform inference with Yi base model</a>.</p>
<p><strong>Note</strong> that the only difference is to set <code>--model <your-model-mount-path>'</code> instead of <code>model <your-model-path></code>.</p>
</details>
### Quick start - conda-lock
<details>
<summary>You can use <code><a href="https://github.com/conda/conda-lock">conda-lock</a></code> to generate fully reproducible lock files for conda environments. ⬇️</summary>
<br>
You can refer to <a href="https://github.com/01-ai/Yi/blob/ebba23451d780f35e74a780987ad377553134f68/conda-lock.yml">conda-lock.yml</a> for the exact versions of the dependencies. Additionally, you can utilize <code><a href="https://mamba.readthedocs.io/en/latest/user_guide/micromamba.html">micromamba</a></code> for installing these dependencies.
<br>
To install the dependencies, follow these steps:
1. Install micromamba by following the instructions available <a href="https://mamba.readthedocs.io/en/latest/installation/micromamba-installation.html">here</a>.
2. Execute <code>micromamba install -y -n yi -f conda-lock.yml</code> to create a conda environment named <code>yi</code> and install the necessary dependencies.
</details>
### Quick start - llama.cpp
<a href="https://github.com/01-ai/Yi/blob/main/docs/README_llama.cpp.md">The following tutorial </a> will guide you through every step of running a quantized model (<a href="https://huggingface.co/XeIaso/yi-chat-6B-GGUF/tree/main">Yi-chat-6B-2bits</a>) locally and then performing inference.
<details>
<summary> Run Yi-chat-6B-2bits locally with llama.cpp: a step-by-step guide. ⬇️</summary>
<br><a href="https://github.com/01-ai/Yi/blob/main/docs/README_llama.cpp.md">This tutorial</a> guides you through every step of running a quantized model (<a href="https://huggingface.co/XeIaso/yi-chat-6B-GGUF/tree/main">Yi-chat-6B-2bits</a>) locally and then performing inference.</p>
- [Step 0: Prerequisites](#step-0-prerequisites)
- [Step 1: Download llama.cpp](#step-1-download-llamacpp)
- [Step 2: Download Yi model](#step-2-download-yi-model)
- [Step 3: Perform inference](#step-3-perform-inference)
#### Step 0: Prerequisites
- This tutorial assumes you use a MacBook Pro with 16GB of memory and an Apple M2 Pro chip.
- Make sure [`git-lfs`](https://git-lfs.com/) is installed on your machine.
#### Step 1: Download `llama.cpp`
To clone the [`llama.cpp`](https://github.com/ggerganov/llama.cpp) repository, run the following command.
```bash
git clone [email protected]:ggerganov/llama.cpp.git
```
#### Step 2: Download Yi model
2.1 To clone [XeIaso/yi-chat-6B-GGUF](https://huggingface.co/XeIaso/yi-chat-6B-GGUF/tree/main) with just pointers, run the following command.
```bash
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/XeIaso/yi-chat-6B-GGUF
```
2.2 To download a quantized Yi model ([yi-chat-6b.Q2_K.gguf](https://huggingface.co/XeIaso/yi-chat-6B-GGUF/blob/main/yi-chat-6b.Q2_K.gguf)), run the following command.
```bash
git-lfs pull --include yi-chat-6b.Q2_K.gguf
```
#### Step 3: Perform inference
To perform inference with the Yi model, you can use one of the following methods.
- [Method 1: Perform inference in terminal](#method-1-perform-inference-in-terminal)
- [Method 2: Perform inference in web](#method-2-perform-inference-in-web)
##### Method 1: Perform inference in terminal
To compile `llama.cpp` using 4 threads and then conduct inference, navigate to the `llama.cpp` directory, and run the following command.
> ##### Tips
>
> - Replace `/Users/yu/yi-chat-6B-GGUF/yi-chat-6b.Q2_K.gguf` with the actual path of your model.
>
> - By default, the model operates in completion mode.
>
> - For additional output customization options (for example, system prompt, temperature, repetition penalty, etc.), run `./main -h` to check detailed descriptions and usage.
```bash
make -j4 && ./main -m /Users/yu/yi-chat-6B-GGUF/yi-chat-6b.Q2_K.gguf -p "How do you feed your pet fox? Please answer this question in 6 simple steps:\nStep 1:" -n 384 -e
...
How do you feed your pet fox? Please answer this question in 6 simple steps:
Step 1: Select the appropriate food for your pet fox. You should choose high-quality, balanced prey items that are suitable for their unique dietary needs. These could include live or frozen mice, rats, pigeons, or other small mammals, as well as fresh fruits and vegetables.
Step 2: Feed your pet fox once or twice a day, depending on the species and its individual preferences. Always ensure that they have access to fresh water throughout the day.
Step 3: Provide an appropriate environment for your pet fox. Ensure it has a comfortable place to rest, plenty of space to move around, and opportunities to play and exercise.
Step 4: Socialize your pet with other animals if possible. Interactions with other creatures can help them develop social skills and prevent boredom or stress.
Step 5: Regularly check for signs of illness or discomfort in your fox. Be prepared to provide veterinary care as needed, especially for common issues such as parasites, dental health problems, or infections.
Step 6: Educate yourself about the needs of your pet fox and be aware of any potential risks or concerns that could affect their well-being. Regularly consult with a veterinarian to ensure you are providing the best care.
...
```
Now you have successfully asked a question to the Yi model and got an answer! 🥳
##### Method 2: Perform inference in web
1. To initialize a lightweight and swift chatbot, run the following command.
```bash
cd llama.cpp
./server --ctx-size 2048 --host 0.0.0.0 --n-gpu-layers 64 --model /Users/yu/yi-chat-6B-GGUF/yi-chat-6b.Q2_K.gguf
```
Then you can get an output like this:
```bash
...
llama_new_context_with_model: n_ctx = 2048
llama_new_context_with_model: freq_base = 5000000.0
llama_new_context_with_model: freq_scale = 1
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M2 Pro
ggml_metal_init: picking default device: Apple M2 Pro
ggml_metal_init: ggml.metallib not found, loading from source
ggml_metal_init: GGML_METAL_PATH_RESOURCES = nil
ggml_metal_init: loading '/Users/yu/llama.cpp/ggml-metal.metal'
ggml_metal_init: GPU name: Apple M2 Pro
ggml_metal_init: GPU family: MTLGPUFamilyApple8 (1008)
ggml_metal_init: hasUnifiedMemory = true
ggml_metal_init: recommendedMaxWorkingSetSize = 11453.25 MB
ggml_metal_init: maxTransferRate = built-in GPU
ggml_backend_metal_buffer_type_alloc_buffer: allocated buffer, size = 128.00 MiB, ( 2629.44 / 10922.67)
llama_new_context_with_model: KV self size = 128.00 MiB, K (f16): 64.00 MiB, V (f16): 64.00 MiB
ggml_backend_metal_buffer_type_alloc_buffer: allocated buffer, size = 0.02 MiB, ( 2629.45 / 10922.67)
llama_build_graph: non-view tensors processed: 676/676
llama_new_context_with_model: compute buffer total size = 159.19 MiB
ggml_backend_metal_buffer_type_alloc_buffer: allocated buffer, size = 156.02 MiB, ( 2785.45 / 10922.67)
Available slots:
-> Slot 0 - max context: 2048
llama server listening at http://0.0.0.0:8080
```
2. To access the chatbot interface, open your web browser and enter `http://0.0.0.0:8080` into the address bar.
3. Enter a question, such as "How do you feed your pet fox? Please answer this question in 6 simple steps" into the prompt window, and you will receive a corresponding answer.
</ul>
</details>
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</p>
### Web demo
You can build a web UI demo for Yi **chat** models (note that Yi base models are not supported in this senario).
[Step 1: Prepare your environment](#step-1-prepare-your-environment).
[Step 2: Download the Yi model](#step-2-download-the-yi-model).
Step 3. To start a web service locally, run the following command.
```bash
python demo/web_demo.py -c <your-model-path>
```
You can access the web UI by entering the address provided in the console into your browser.
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</p>
### Fine-tuning
```bash
bash finetune/scripts/run_sft_Yi_6b.sh
```
Once finished, you can compare the finetuned model and the base model with the following command:
```bash
bash finetune/scripts/run_eval.sh
```
<details style="display: inline;"><summary>For advanced usage (like fine-tuning based on your custom data), see the explanations below. ⬇️ </summary> <ul>
### Finetune code for Yi 6B and 34B
#### Preparation
##### From Image
By default, we use a small dataset from [BAAI/COIG](https://huggingface.co/datasets/BAAI/COIG) to finetune the base model.
You can also prepare your customized dataset in the following `jsonl` format:
```json
{ "prompt": "Human: Who are you? Assistant:", "chosen": "I'm Yi." }
```
And then mount them in the container to replace the default ones:
```bash
docker run -it \
-v /path/to/save/finetuned/model/:/finetuned-model \
-v /path/to/train.jsonl:/yi/finetune/data/train.json \
-v /path/to/eval.jsonl:/yi/finetune/data/eval.json \
ghcr.io/01-ai/yi:latest \
bash finetune/scripts/run_sft_Yi_6b.sh
```
##### From Local Server
Make sure you have conda. If not, use
```bash
mkdir -p ~/miniconda3
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
rm -rf ~/miniconda3/miniconda.sh
~/miniconda3/bin/conda init bash
source ~/.bashrc
```
Then, create a conda env:
```bash
conda create -n dev_env python=3.10 -y
conda activate dev_env
pip install torch==2.0.1 deepspeed==0.10 tensorboard transformers datasets sentencepiece accelerate ray==2.7
```
#### Hardware Setup
For the Yi-6B model, a node with 4 GPUs, each with GPU memory larger than 60GB, is recommended.
For the Yi-34B model, because the usage of the zero-offload technique consumes a lot of CPU memory, please be careful to limit the number of GPUs in the 34B finetune training. Please use CUDA_VISIBLE_DEVICES to limit the number of GPUs (as shown in scripts/run_sft_Yi_34b.sh).
A typical hardware setup for finetuning the 34B model is a node with 8 GPUs (limited to 4 in running by CUDA_VISIBLE_DEVICES=0,1,2,3), each with GPU memory larger than 80GB, and total CPU memory larger than 900GB.
#### Quick Start
Download a LLM-base model to MODEL_PATH (6B and 34B). A typical folder of models is like:
```bash
|-- $MODEL_PATH
| |-- config.json
| |-- pytorch_model-00001-of-00002.bin
| |-- pytorch_model-00002-of-00002.bin
| |-- pytorch_model.bin.index.json
| |-- tokenizer_config.json
| |-- tokenizer.model
| |-- ...
```
Download a dataset from huggingface to local storage DATA_PATH, e.g. Dahoas/rm-static.
```bash
|-- $DATA_PATH
| |-- data
| | |-- train-00000-of-00001-2a1df75c6bce91ab.parquet
| | |-- test-00000-of-00001-8c7c51afc6d45980.parquet
| |-- dataset_infos.json
| |-- README.md
```
`finetune/yi_example_dataset` has example datasets, which are modified from [BAAI/COIG](https://huggingface.co/datasets/BAAI/COIG)
```bash
|-- $DATA_PATH
|--data
|-- train.jsonl
|-- eval.jsonl
```
`cd` into the scripts folder, copy and paste the script, and run. For example:
```bash
cd finetune/scripts
bash run_sft_Yi_6b.sh
```
For the Yi-6B base model, setting training_debug_steps=20 and num_train_epochs=4 can output a chat model, which takes about 20 minutes.
For the Yi-34B base model, it takes a relatively long time for initialization. Please be patient.
#### Evaluation
```bash
cd finetune/scripts
bash run_eval.sh
```
Then you'll see the answer from both the base model and the finetuned model.
</ul>
</details>
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</p>
### Quantization
#### GPT-Q
```bash
python quantization/gptq/quant_autogptq.py \
--model /base_model \
--output_dir /quantized_model \
--trust_remote_code
```
Once finished, you can then evaluate the resulting model as follows:
```bash
python quantization/gptq/eval_quantized_model.py \
--model /quantized_model \
--trust_remote_code
```
<details style="display: inline;"><summary>For details, see the explanations below. ⬇️</summary> <ul>
#### GPT-Q quantization
[GPT-Q](https://github.com/IST-DASLab/gptq) is a PTQ (Post-Training Quantization)
method. It saves memory and provides potential speedups while retaining the accuracy
of the model.
Yi models can be GPT-Q quantized without a lot of efforts.
We provide a step-by-step tutorial below.
To run GPT-Q, we will use [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) and
[exllama](https://github.com/turboderp/exllama).
And the huggingface transformers has integrated optimum and auto-gptq to perform
GPTQ quantization on language models.
##### Do Quantization
The `quant_autogptq.py` script is provided for you to perform GPT-Q quantization:
```bash
python quant_autogptq.py --model /base_model \
--output_dir /quantized_model --bits 4 --group_size 128 --trust_remote_code
```
##### Run Quantized Model
You can run a quantized model using the `eval_quantized_model.py`:
```bash
python eval_quantized_model.py --model /quantized_model --trust_remote_code
```
</ul>
</details>
#### AWQ
```bash
python quantization/awq/quant_autoawq.py \
--model /base_model \
--output_dir /quantized_model \
--trust_remote_code
```
Once finished, you can then evaluate the resulting model as follows:
```bash
python quantization/awq/eval_quantized_model.py \
--model /quantized_model \
--trust_remote_code
```
<details style="display: inline;"><summary>For details, see the explanations below. ⬇️</summary> <ul>
#### AWQ quantization
[AWQ](https://github.com/mit-han-lab/llm-awq) is a PTQ (Post-Training Quantization)
method. It's an efficient and accurate low-bit weight quantization (INT3/4) for LLMs.
Yi models can be AWQ quantized without a lot of efforts.
We provide a step-by-step tutorial below.
To run AWQ, we will use [AutoAWQ](https://github.com/casper-hansen/AutoAWQ).
##### Do Quantization
The `quant_autoawq.py` script is provided for you to perform AWQ quantization:
```bash
python quant_autoawq.py --model /base_model \
--output_dir /quantized_model --bits 4 --group_size 128 --trust_remote_code
```
##### Run Quantized Model
You can run a quantized model using the `eval_quantized_model.py`:
```bash
python eval_quantized_model.py --model /quantized_model --trust_remote_code
```
</ul>
</details>
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</p>
### Deployment
If you want to deploy Yi models, make sure you meet the software and hardware requirements.
#### Software requirements
Before using Yi quantized models, make sure you've installed the correct software listed below.
| Model | Software
|---|---
Yi 4-bit quantized models | [AWQ and CUDA](https://github.com/casper-hansen/AutoAWQ?tab=readme-ov-file#install-from-pypi)
Yi 8-bit quantized models | [GPTQ and CUDA](https://github.com/PanQiWei/AutoGPTQ?tab=readme-ov-file#quick-installation)
#### Hardware requirements
Before deploying Yi in your environment, make sure your hardware meets the following requirements.
##### Chat models
| Model | Minimum VRAM | Recommended GPU Example |
|:----------------------|:--------------|:-------------------------------------:|
| Yi-6B-Chat | 15 GB | 1 x RTX 3090 (24 GB) <br> 1 x RTX 4090 (24 GB) <br> 1 x A10 (24 GB) <br> 1 x A30 (24 GB) |
| Yi-6B-Chat-4bits | 4 GB | 1 x RTX 3060 (12 GB)<br> 1 x RTX 4060 (8 GB) |
| Yi-6B-Chat-8bits | 8 GB | 1 x RTX 3070 (8 GB) <br> 1 x RTX 4060 (8 GB) |
| Yi-34B-Chat | 72 GB | 4 x RTX 4090 (24 GB)<br> 1 x A800 (80GB) |
| Yi-34B-Chat-4bits | 20 GB | 1 x RTX 3090 (24 GB) <br> 1 x RTX 4090 (24 GB) <br> 1 x A10 (24 GB) <br> 1 x A30 (24 GB) <br> 1 x A100 (40 GB) |
| Yi-34B-Chat-8bits | 38 GB | 2 x RTX 3090 (24 GB) <br> 2 x RTX 4090 (24 GB)<br> 1 x A800 (40 GB) |
Below are detailed minimum VRAM requirements under different batch use cases.
| Model | batch=1 | batch=4 | batch=16 | batch=32 |
| ----------------------- | ------- | ------- | -------- | -------- |
| Yi-6B-Chat | 12 GB | 13 GB | 15 GB | 18 GB |
| Yi-6B-Chat-4bits | 4 GB | 5 GB | 7 GB | 10 GB |
| Yi-6B-Chat-8bits | 7 GB | 8 GB | 10 GB | 14 GB |
| Yi-34B-Chat | 65 GB | 68 GB | 76 GB | > 80 GB |
| Yi-34B-Chat-4bits | 19 GB | 20 GB | 30 GB | 40 GB |
| Yi-34B-Chat-8bits | 35 GB | 37 GB | 46 GB | 58 GB |
##### Base models
| Model | Minimum VRAM | Recommended GPU Example |
|----------------------|--------------|:-------------------------------------:|
| Yi-6B | 15 GB | 1 x RTX 3090 (24 GB) <br> 1 x RTX 4090 (24 GB) <br> 1 x A10 (24 GB) <br> 1 x A30 (24 GB) |
| Yi-6B-200K | 50 GB | 1 x A800 (80 GB) |
| Yi-9B | 20 GB | 1 x RTX 4090 (24 GB) |
| Yi-34B | 72 GB | 4 x RTX 4090 (24 GB) <br> 1 x A800 (80 GB) |
| Yi-34B-200K | 200 GB | 4 x A800 (80 GB) |
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</p>
### FAQ
<details>
<summary> If you have any questions while using the Yi series models, the answers provided below could serve as a helpful reference for you. ⬇️</summary>
<br>
#### 💡Fine-tuning
- <strong>Base model or Chat model - which to fine-tune?</strong>
<br>The choice of pre-trained language model for fine-tuning hinges on the computational resources you have at your disposal and the particular demands of your task.
- If you are working with a substantial volume of fine-tuning data (say, over 10,000 samples), the Base model could be your go-to choice.
- On the other hand, if your fine-tuning data is not quite as extensive, opting for the Chat model might be a more fitting choice.
- It is generally advisable to fine-tune both the Base and Chat models, compare their performance, and then pick the model that best aligns with your specific requirements.
- <strong>Yi-34B versus Yi-34B-Chat for full-scale fine-tuning - what is the difference?</strong>
<br>
The key distinction between full-scale fine-tuning on `Yi-34B`and `Yi-34B-Chat` comes down to the fine-tuning approach and outcomes.
- Yi-34B-Chat employs a Special Fine-Tuning (SFT) method, resulting in responses that mirror human conversation style more closely.
- The Base model's fine-tuning is more versatile, with a relatively high performance potential.
- If you are confident in the quality of your data, fine-tuning with `Yi-34B` could be your go-to.
- If you are aiming for model-generated responses that better mimic human conversational style, or if you have doubts about your data quality, `Yi-34B-Chat` might be your best bet.
#### 💡Quantization
- <strong>Quantized model versus original model - what is the performance gap?</strong>
- The performance variance is largely contingent on the quantization method employed and the specific use cases of these models. For instance, when it comes to models provided by the AWQ official, from a Benchmark standpoint, quantization might result in a minor performance drop of a few percentage points.
- Subjectively speaking, in situations like logical reasoning, even a 1% performance shift could impact the accuracy of the output results.
#### 💡General
- <strong>Where can I source fine-tuning question answering datasets?</strong>
- You can find fine-tuning question answering datasets on platforms like Hugging Face, with datasets like [m-a-p/COIG-CQIA](https://huggingface.co/datasets/m-a-p/COIG-CQIA) readily available.
- Additionally, Github offers fine-tuning frameworks, such as [hiyouga/LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory), which integrates pre-made datasets.
- <strong>What is the GPU memory requirement for fine-tuning Yi-34B FP16?</strong>
<br>
The GPU memory needed for fine-tuning 34B FP16 hinges on the specific fine-tuning method employed. For full parameter fine-tuning, you'll need 8 GPUs each with 80 GB; however, more economical solutions like Lora require less. For more details, check out [hiyouga/LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory). Also, consider using BF16 instead of FP16 for fine-tuning to optimize performance.
- <strong>Are there any third-party platforms that support chat functionality for the Yi-34b-200k model?</strong>
<br>
If you're looking for third-party Chats, options include [fireworks.ai](https://fireworks.ai/login?callbackURL=https://fireworks.ai/models/fireworks/yi-34b-chat).
</details>
### Learning hub
<details>
<summary> If you want to learn Yi, you can find a wealth of helpful educational resources here. ⬇️</summary>
<br>
Welcome to the Yi learning hub!
Whether you're a seasoned developer or a newcomer, you can find a wealth of helpful educational resources to enhance your understanding and skills with Yi models, including insightful blog posts, comprehensive video tutorials, hands-on guides, and more.
The content you find here has been generously contributed by knowledgeable Yi experts and passionate enthusiasts. We extend our heartfelt gratitude for your invaluable contributions!
At the same time, we also warmly invite you to join our collaborative effort by contributing to Yi. If you have already made contributions to Yi, please don't hesitate to showcase your remarkable work in the table below.
With all these resources at your fingertips, you're ready to start your exciting journey with Yi. Happy learning! 🥳
#### Tutorials
##### Blog tutorials
| Deliverable | Date | Author |
| ------------------------------------------------------------ | ---------- | ------------------------------------------------------------ |
| [使用 Dify、Meilisearch、零一万物模型实现最简单的 RAG 应用（三）：AI 电影推荐](https://mp.weixin.qq.com/s/Ri2ap9_5EMzdfiBhSSL_MQ) | 2024-05-20 | [苏洋](https://github.com/soulteary) |
| [使用autodl服务器，在A40显卡上运行， Yi-34B-Chat-int4模型，并使用vllm优化加速，显存占用42G，速度18 words-s](https://blog.csdn.net/freewebsys/article/details/134698597?ops_request_misc=%7B%22request%5Fid%22%3A%22171636168816800227489911%22%2C%22scm%22%3A%2220140713.130102334.pc%5Fblog.%22%7D&request_id=171636168816800227489911&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_ecpm_v1~times_rank-17-134698597-null-null.nonecase&utm_term=Yi大模型&spm=1018.2226.3001.4450) | 2024-05-20 | [fly-iot](https://gitee.com/fly-iot) |
| [Yi-VL 最佳实践](https://modelscope.cn/docs/yi-vl最佳实践) | 2024-05-20 | [ModelScope](https://github.com/modelscope) |
| [一键运行零一万物新鲜出炉Yi-1.5-9B-Chat大模型](https://mp.weixin.qq.com/s/ntMs2G_XdWeM3I6RUOBJrA) | 2024-05-13 | [Second State](https://github.com/second-state) |
| [零一万物开源Yi-1.5系列大模型](https://mp.weixin.qq.com/s/d-ogq4hcFbsuL348ExJxpA) | 2024-05-13 | [刘聪](https://github.com/liucongg) |
| [零一万物Yi-1.5系列模型发布并开源！ 34B-9B-6B 多尺寸，魔搭社区推理微调最佳实践教程来啦！](https://mp.weixin.qq.com/s/3wD-0dCgXB646r720o8JAg) | 2024-05-13 | [ModelScope](https://github.com/modelscope) |
| [Yi-34B 本地部署简单测试](https://blog.csdn.net/arkohut/article/details/135331469?ops_request_misc=%7B%22request%5Fid%22%3A%22171636390616800185813639%22%2C%22scm%22%3A%2220140713.130102334.pc%5Fblog.%22%7D&request_id=171636390616800185813639&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_ecpm_v1~times_rank-10-135331469-null-null.nonecase&utm_term=Yi大模型&spm=1018.2226.3001.4450) | 2024-05-13 | [漆妮妮](https://space.bilibili.com/1262370256) |
| [驾辰龙跨Llama持Wasm，玩转Yi模型迎新春过大年（上）](https://blog.csdn.net/weixin_53443275/article/details/136091398?ops_request_misc=%7B%22request%5Fid%22%3A%22171636390616800185813639%22%2C%22scm%22%3A%2220140713.130102334.pc%5Fblog.%22%7D&request_id=171636390616800185813639&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_ecpm_v1~times_rank-5-136091398-null-null.nonecase&utm_term=Yi大模型&spm=1018.2226.3001.4450) | 2024-05-13 | [Words worth](https://blog.csdn.net/weixin_53443275?type=blog) |
| [驾辰龙跨Llama持Wasm，玩转Yi模型迎新春过大年（下篇）](https://blog.csdn.net/weixin_53443275/article/details/136096309) | 2024-05-13 | [Words worth](https://blog.csdn.net/weixin_53443275?type=blog) |
| [Ollama新增两个命令，开始支持零一万物Yi-1.5系列模型](https://mp.weixin.qq.com/s/bBgzGJvUqIohodcy9U-pFw) | 2024-05-13 | AI工程师笔记 |
| [使用零一万物 200K 模型和 Dify 快速搭建模型应用](https://zhuanlan.zhihu.com/p/686774859) | 2024-05-13 | [苏洋](https://github.com/soulteary) |
| [(持更) 零一万物模型折腾笔记：社区 Yi-34B 微调模型使用](https://zhuanlan.zhihu.com/p/671549900) | 2024-05-13 | [苏洋](https://github.com/soulteary) |
| [Python+ERNIE-4.0-8K-Yi-34B-Chat大模型初探](https://mp.weixin.qq.com/s/WaygSfn5T8ZPB1mPdGADEQ) | 2024-05-11 | 江湖评谈 |
| [技术布道 Vue及Python调用零一万物模型和Prompt模板（通过百度千帆大模型平台）](https://blog.csdn.net/ucloud2012/article/details/137187469) | 2024-05-11 | [MumuLab](https://blog.csdn.net/ucloud2012?type=blog) |
| [多模态大模型Yi-VL-plus体验 效果很棒](https://zhuanlan.zhihu.com/p/694736111) | 2024-04-27 | [大家好我是爱因](https://www.zhihu.com/people/iamein) |
| [使用autodl服务器，两个3090显卡上运行， Yi-34B-Chat-int4模型，并使用vllm优化加速，显存占用42G，速度23 words-s](https://blog.csdn.net/freewebsys/article/details/134725765?ops_request_misc=%7B%22request%5Fid%22%3A%22171636356716800211598950%22%2C%22scm%22%3A%2220140713.130102334.pc%5Fblog.%22%7D&request_id=171636356716800211598950&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_ecpm_v1~times_rank-9-134725765-null-null.nonecase&utm_term=Yi大模型&spm=1018.2226.3001.4450) | 2024-04-27 | [fly-iot](https://gitee.com/fly-iot) |
| [Getting Started with Yi-1.5-9B-Chat](https://www.secondstate.io/articles/yi-1.5-9b-chat/) | 2024-04-27 | [Second State](https://github.com/second-state) |
| [基于零一万物yi-vl-plus大模型简单几步就能批量生成Anki图片笔记](https://mp.weixin.qq.com/s/_ea6g0pzzeO4WyYtuWycWQ) | 2024-04-24 | [正经人王同学](https://github.com/zjrwtx) |
| [【AI开发：语言】一、Yi-34B超大模型本地部署CPU和GPU版](https://blog.csdn.net/alarey/article/details/137769471?ops_request_misc=%7B%22request%5Fid%22%3A%22171636168816800227489911%22%2C%22scm%22%3A%2220140713.130102334.pc%5Fblog.%22%7D&request_id=171636168816800227489911&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_ecpm_v1~times_rank-16-137769471-null-null.nonecase&utm_term=Yi大模型&spm=1018.2226.3001.4450) | 2024-04-21 | [My的梦想已实现](https://blog.csdn.net/alarey?type=blog) |
| [【Yi-34B-Chat-Int4】使用4个2080Ti显卡11G版本，运行Yi-34B模型，5年前老显卡是支持的，可以正常运行，速度 21 words-s，vllm要求算力在7以上的显卡就可以](https://blog.csdn.net/freewebsys/article/details/134754086) | 2024-03-22 | [fly-iot](https://gitee.com/fly-iot) |
| [零一万物大模型部署+微调总结](https://blog.csdn.net/v_wus/article/details/135704126?ops_request_misc=%7B%22request%5Fid%22%3A%22171636168816800227489911%22%2C%22scm%22%3A%2220140713.130102334.pc%5Fblog.%22%7D&request_id=171636168816800227489911&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_ecpm_v1~times_rank-18-135704126-null-null.nonecase&utm_term=Yi大模型&spm=1018.2226.3001.4450) | 2024-03-22 | [v_wus](https://blog.csdn.net/v_wus?type=blog) |
| [零一万物Yi大模型vllm推理时Yi-34B或Yi-6bchat重复输出的解决方案](https://blog.csdn.net/qq_39667443/article/details/136028776?ops_request_misc=%7B%22request%5Fid%22%3A%22171636168816800227489911%22%2C%22scm%22%3A%2220140713.130102334.pc%5Fblog.%22%7D&request_id=171636168816800227489911&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_ecpm_v1~times_rank-6-136028776-null-null.nonecase&utm_term=Yi大模型&spm=1018.2226.3001.4450) | 2024-03-02 | [郝铠锋](https://blog.csdn.net/qq_39667443?type=blog) |
| [Yi-34B微调训练](https://blog.csdn.net/lsjlnd/article/details/135336984?ops_request_misc=%7B%22request%5Fid%22%3A%22171636343416800188513953%22%2C%22scm%22%3A%2220140713.130102334.pc%5Fblog.%22%7D&request_id=171636343416800188513953&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_ecpm_v1~times_rank-12-135336984-null-null.nonecase&utm_term=Yi大模型&spm=1018.2226.3001.4450) | 2024-03-02 | [lsjlnd](https://blog.csdn.net/lsjlnd?type=blog) |
| [实测零一万物Yi-VL多模态语言模型：能准确“识图吃瓜”](https://mp.weixin.qq.com/s/fu4O9XvJ03JhimsEyI-SsQ) | 2024-02-02 | [苏洋](https://github.com/soulteary) |
| [零一万物开源Yi-VL多模态大模型，魔搭社区推理&微调最佳实践来啦！](https://zhuanlan.zhihu.com/p/680098411) | 2024-01-26 | [ModelScope](https://github.com/modelscope) |
| [单卡 3 小时训练 Yi-6B 大模型 Agent：基于 Llama Factory 实战](https://zhuanlan.zhihu.com/p/678989191) | 2024-01-22 | [郑耀威](https://github.com/hiyouga) |
| [零一科技Yi-34B Chat大模型环境搭建&推理](https://blog.csdn.net/zzq1989_/article/details/135597181?ops_request_misc=%7B%22request%5Fid%22%3A%22171636168816800227489911%22%2C%22scm%22%3A%2220140713.130102334.pc%5Fblog.%22%7D&request_id=171636168816800227489911&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_ecpm_v1~times_rank-8-135597181-null-null.nonecase&utm_term=Yi大模型&spm=1018.2226.3001.4450) | 2024-01-15 | [要养家的程序员](https://blog.csdn.net/zzq1989_?type=blog) |
| [基于LLaMA Factory，单卡3小时训练专属大模型 Agent](https://blog.csdn.net/m0_59596990/article/details/135760285?ops_request_misc=%7B%22request%5Fid%22%3A%22171636343416800188513953%22%2C%22scm%22%3A%2220140713.130102334.pc%5Fblog.%22%7D&request_id=171636343416800188513953&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_ecpm_v1~times_rank-10-135760285-null-null.nonecase&utm_term=Yi大模型&spm=1018.2226.3001.4450) | 2024-01-15 | [机器学习社区](https://blog.csdn.net/m0_59596990?type=blog) |
| [双卡 3080ti 部署 Yi-34B 大模型 - Gradio + vLLM 踩坑全记录](https://blog.csdn.net/arkohut/article/details/135321242?ops_request_misc=%7B%22request%5Fid%22%3A%22171636168816800227489911%22%2C%22scm%22%3A%2220140713.130102334.pc%5Fblog.%22%7D&request_id=171636168816800227489911&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_ecpm_v1~times_rank-10-135321242-null-null.nonecase&utm_term=Yi大模型&spm=1018.2226.3001.4450) | 2024-01-02 | [漆妮妮](https://space.bilibili.com/1262370256) |
| [【大模型部署实践-3】3个能在3090上跑起来的4bits量化Chat模型（baichuan2-13b、InternLM-20b、Yi-34b）](https://blog.csdn.net/qq_40302568/article/details/135040985?ops_request_misc=%7B%22request%5Fid%22%3A%22171636168816800227489911%22%2C%22scm%22%3A%2220140713.130102334.pc%5Fblog.%22%7D&request_id=171636168816800227489911&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_ecpm_v1~times_rank-30-135040985-null-null.nonecase&utm_term=Yi大模型&spm=1018.2226.3001.4450) | 2024-01-02 | [aq_Seabiscuit](https://blog.csdn.net/qq_40302568?type=blog) |
| [只需 24G 显存，用 vllm 跑起来 Yi-34B 中英双语大模型](https://blog.csdn.net/arkohut/article/details/135274973) | 2023-12-28 | [漆妮妮](https://space.bilibili.com/1262370256) |
| [零一万物模型官方 Yi-34B 模型本地离线运行部署使用笔记（物理机和docker两种部署方式），200K 超长文本内容，34B 干翻一众 70B 模型，打榜分数那么高，这模型到底行不行？](https://blog.csdn.net/u014374009/article/details/136327696) | 2023-12-28 | [代码讲故事](https://blog.csdn.net/u014374009?type=blog) |
| [LLM - 大模型速递之 Yi-34B 入门与 LoRA 微调](https://blog.csdn.net/BIT_666/article/details/134990402) | 2023-12-18 | [BIT_666](https://bitddd.blog.csdn.net/?type=blog) |
| [通过vllm框架进行大模型推理](https://blog.csdn.net/weixin_45920955/article/details/135300561?ops_request_misc=%7B%22request%5Fid%22%3A%22171636343416800188513953%22%2C%22scm%22%3A%2220140713.130102334.pc%5Fblog.%22%7D&request_id=171636343416800188513953&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_ecpm_v1~times_rank-13-135300561-null-null.nonecase&utm_term=Yi大模型&spm=1018.2226.3001.4450) | 2023-12-18 | [土山炮](https://blog.csdn.net/weixin_45920955?type=blog) |
| [CPU 混合推理，非常见大模型量化方案：“二三五六” 位量化方案](https://zhuanlan.zhihu.com/p/671698216) | 2023-12-12 | [苏洋](https://github.com/soulteary) |
| [零一万物模型折腾笔记：官方 Yi-34B 模型基础使用](https://zhuanlan.zhihu.com/p/671387298) | 2023-12-10 | [苏洋](https://github.com/soulteary) |
| [Running Yi-34B-Chat locally using LlamaEdge](https://www.secondstate.io/articles/yi-34b/) | 2023-11-30 | [Second State](https://github.com/second-state) |
| [本地运行零一万物 34B 大模型，使用 Llama.cpp & 21G 显存](https://zhuanlan.zhihu.com/p/668921042) | 2023-11-26 | [苏洋](https://github.com/soulteary) |
##### GitHub Project
| Deliverable | Date | Author |
| ------------------------------------------------------------ | ---------- | ------------------------------------------- |
| [yi-openai-proxy](https://github.com/soulteary/yi-openai-proxy) | 2024-05-11 | [苏洋](https://github.com/soulteary) |
| [基于零一万物 Yi 模型和 B 站构建大语言模型高质量训练数据集](https://github.com/zjrwtx/bilibiliQA_databuilder) | 2024-04-29 | [正经人王同学](https://github.com/zjrwtx) |
| [基于视频网站和零一万物大模型构建大语言模型高质量训练数据集](https://github.com/zjrwtx/VideoQA_databuilder) | 2024-04-25 | [正经人王同学](https://github.com/zjrwtx) |
| [基于零一万物yi-34b-chat-200k输入任意文章地址，点击按钮即可生成无广告或推广内容的简要笔记，并生成分享图给好友](https://github.com/zjrwtx/open_summary) | 2024-04-24 | [正经人王同学](https://github.com/zjrwtx) |
| [Food-GPT-Yi-model](https://github.com/ThisisHubert/FoodGPT-Yi-model) | 2024-04-21 | [Hubert S](https://github.com/ThisisHubert) |
##### Video tutorials
| Deliverable | Date | Author |
| ------------------------------------------------------------ | ---------- | ------------------------------------------------------------ |
| [Run dolphin-2.2-yi-34b on IoT Devices](https://www.youtube.com/watch?v=NJ89T5mO25Y) | 2023-11-30 | [Second State](https://github.com/second-state) |
| [只需 24G 显存，用 vllm 跑起来 Yi-34B 中英双语大模型](https://www.bilibili.com/video/BV17t4y1f7Ee/) | 2023-12-28 | [漆妮妮](https://space.bilibili.com/1262370256) |
| [Install Yi 34B Locally - Chinese English Bilingual LLM](https://www.youtube.com/watch?v=CVQvj4Wrh4w&t=476s) | 2023-11-05 | [Fahd Mirza](https://www.youtube.com/@fahdmirza) |
| [Dolphin Yi 34b - Brand New Foundational Model TESTED](https://www.youtube.com/watch?v=On3Zuv27V3k&t=85s) | 2023-11-27 | [Matthew Berman](https://www.youtube.com/@matthew_berman) |
| [Yi-VL-34B 多模态大模型 - 用两张 A40 显卡跑起来](https://www.bilibili.com/video/BV1Q5411y7AG/) | 2024-01-28 | [漆妮妮](https://space.bilibili.com/1262370256) |
| [4060Ti 16G显卡安装零一万物最新开源的Yi-1.5版大语言模型](https://www.bilibili.com/video/BV16i421X7Jx/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-05-14 | [titan909](https://space.bilibili.com/526393761) |
| [Yi-1.5: True Apache 2.0 Competitor to LLAMA-3](https://www.youtube.com/watch?v=KCDYrfWeTRc) | 2024-05-13 | [Prompt Engineering](https://www.youtube.com/@engineerprompt) |
| [Install Yi-1.5 Model Locally - Beats Llama 3 in Various Benchmarks](https://www.youtube.com/watch?v=Ba-G7Il0UkA) | 2024-05-13 | [Fahd Mirza](https://www.youtube.com/@fahdmirza) |
| [how to install Ollama and run Yi 6B](https://www.youtube.com/watch?v=4Jnar7OUHqQ) | 2024-05-13 | [Ridaa Davids](https://www.youtube.com/@quantanovabusiness) |
| [地表最强混合智能AI助手：llama3_70B+Yi_34B+Qwen1.5_110B](https://www.bilibili.com/video/BV1Xm411C7V1/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-05-04 | [朱扎特](https://space.bilibili.com/494512200?spm_id_from=333.788.0.0) |
| [ChatDoc学术论文辅助--基于Yi-34B和langchain进行PDF知识库问答](https://www.bilibili.com/video/BV11i421C7B5/?spm_id_from=333.999.0.0&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-05-03 | [朱扎特](https://space.bilibili.com/494512200?spm_id_from=333.788.0.0) |
| [基于Yi-34B的领域知识问答项目演示](https://www.bilibili.com/video/BV1zZ42177ZA/?spm_id_from=333.999.0.0&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-05-02 | [朱扎特](https://space.bilibili.com/494512200?spm_id_from=333.788.0.0) |
| [使用RTX4090+GaLore算法 全参微调Yi-6B大模型](https://www.bilibili.com/video/BV1ax4y1U7Ep/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-03-24 | [小工蚂创始人](https://space.bilibili.com/478674499?spm_id_from=333.788.0.0) |
| [无内容审查NSFW大语言模型Yi-34B-Chat蒸馏版测试,RolePlay,《天龙八部》马夫人康敏,本地GPU,CPU运行](https://www.youtube.com/watch?v=VL-W0TnLCns) | 2024-03-20 | [刘悦的技术博客](https://v3u.cn/) |
| [无内容审查NSFW大语言模型整合包,Yi-34B-Chat,本地CPU运行,角色扮演潘金莲](https://www.youtube.com/watch?v=rBvbgwz3oHM) | 2024-03-16 | [刘悦的技术博客](https://v3u.cn/) |
| [量化 Yi-34B-Chat 并在单卡 RTX 4090 使用 vLLM 部署](https://www.bilibili.com/video/BV1jx421y7xj/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-03-05 | [白鸽巢](https://space.bilibili.com/138938660?spm_id_from=333.788.0.0) |
| [Yi-VL-34B（5）：使用3个3090显卡24G版本，运行Yi-VL-34B模型，支持命令行和web界面方式，理解图片的内容转换成文字](https://www.bilibili.com/video/BV1BB421z7oA/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-02-27 | [fly-iot](https://gitee.com/fly-iot) |
| [Win环境KoboldCpp本地部署大语言模型进行各种角色扮演游戏](https://www.bilibili.com/video/BV14J4m1e77f/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-02-25 | [魚蟲蟲](https://space.bilibili.com/431981179?spm_id_from=333.788.0.0) |
| [无需显卡本地部署Yi-34B-Chat进行角色扮演游戏 P2](https://www.bilibili.com/video/BV19v421677y/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-02-23 | [魚蟲蟲](https://space.bilibili.com/431981179?spm_id_from=333.788.0.0) |
| [【wails】（2）：使用go-llama.cpp 运行 yi-01-6b大模型，使用本地CPU运行，速度还可以，等待下一版本更新](https://www.bilibili.com/video/BV194421F7Fy/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-02-20 | [fly-iot](https://gitee.com/fly-iot) |
| [【xinference】（6）：在autodl上，使用xinference部署yi-vl-chat和qwen-vl-chat模型，可以使用openai调用成功](https://www.bilibili.com/video/BV19Z421z7cv/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-02-06 | [fly-iot](https://gitee.com/fly-iot) |
| [无需显卡本地部署Yi-34B-Chat进行角色扮演游戏 P1](https://www.bilibili.com/video/BV1tU421o7Co/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-02-05 | [魚蟲蟲](https://space.bilibili.com/431981179?spm_id_from=333.788.0.0) |
| [2080Ti部署YI-34B大模型 xinference-oneapi-fastGPT本地知识库使用指南](https://www.bilibili.com/video/BV1hC411z7xu/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-01-30 | [小饭护法要转码](https://space.bilibili.com/39486865?spm_id_from=333.788.0.0) |
| [Best Story Writing AI Model - Install Yi 6B 200K Locally on Windows](https://www.youtube.com/watch?v=cZs2jRtl0bs) | 2024-01-22 | [Fahd Mirza](https://www.youtube.com/@fahdmirza) |
| [Mac 本地运行大语言模型方法与常见问题指南（Yi 34B 模型+32 GB 内存测试）](https://www.bilibili.com/video/BV1VT4y1b7Th/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-01-21 | [小吴苹果机器人](https://space.bilibili.com/1732749682?spm_id_from=333.788.0.0) |
| [【Dify知识库】（11）：Dify0.4.9改造支持MySQL，成功接入yi-6b 做对话，本地使用fastchat启动，占8G显存，完成知识库配置](https://www.bilibili.com/video/BV1ia4y1y7JH/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-01-21 | [fly-iot](https://gitee.com/fly-iot) |
| [这位LLM先生有点暴躁,用的是YI-6B的某个量化版,#LLM #大语言模型 #暴躁老哥](https://www.youtube.com/watch?v=eahXJrdtQuc) | 2024-01-20 | [晓漫吧](https://www.youtube.com/@xiaomanba) |
| [大模型推理 NvLink 桥接器有用吗｜双卡 A6000 测试一下](https://www.bilibili.com/video/BV1AW4y1w7DC/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-01-17 | [漆妮妮](https://space.bilibili.com/1262370256) |
| [大模型推理 A40 vs A6000 谁更强 - 对比 Yi-34B 的单、双卡推理性能](https://www.bilibili.com/video/BV1aK4y1z7GF/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-01-15 | [漆妮妮](https://space.bilibili.com/1262370256) |
| [C-Eval 大语言模型评测基准- 用 LM Evaluation Harness + vLLM 跑起来](https://www.bilibili.com/video/BV1Yw411g7ZL/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-01-11 | [漆妮妮](https://space.bilibili.com/1262370256) |
| [双显卡部署 Yi-34B 大模型 - vLLM + Gradio 踩坑记录](https://www.bilibili.com/video/BV1p94y1c7ak/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2024-01-01 | [漆妮妮](https://space.bilibili.com/1262370256) |
| [手把手教学！使用 vLLM 快速部署 Yi-34B-Chat](https://www.bilibili.com/video/BV1ew41157Mk/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2023-12-26 | [白鸽巢](https://space.bilibili.com/138938660?spm_id_from=333.788.0.0) |
| [如何训练企业自己的大语言模型？Yi-6B LORA微调演示 #小工蚁](https://www.bilibili.com/video/BV1uc41117zz/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2023-12-21 | [小工蚂创始人](https://space.bilibili.com/478674499?spm_id_from=333.788.0.0) |
| [Yi-34B（4）：使用4个2080Ti显卡11G版本，运行Yi-34B模型，5年前老显卡是支持的，可以正常运行，速度 21 words/s](https://www.bilibili.com/video/BV1nj41157L3/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2023-12-02 | [fly-iot](https://gitee.com/fly-iot) |
| [使用autodl服务器，RTX 3090 * 3 显卡上运行， Yi-34B-Chat模型，显存占用60G](https://www.bilibili.com/video/BV1BM411R7ae/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2023-12-01 | [fly-iot](https://gitee.com/fly-iot) |
| [使用autodl服务器，两个3090显卡上运行， Yi-34B-Chat-int4模型，用vllm优化，增加 --num-gpu 2，速度23 words/s](https://www.bilibili.com/video/BV1Hu4y1L7BH/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2023-12-01 | [fly-iot](https://gitee.com/fly-iot) |
| [Yi大模型一键本地部署 技术小白玩转AI](https://www.bilibili.com/video/BV16H4y117md/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2023-12-01 | [技术小白玩转AI](https://space.bilibili.com/3546586137234288?spm_id_from=333.788.0.0) |
| [01.AI's Yi-6B: Overview and Fine-Tuning](https://www.youtube.com/watch?v=mye-UOkAliQ) | 2023-11-28 | [AI Makerspace](https://www.youtube.com/@AI-Makerspace) |
| [Yi 34B Chat LLM outperforms Llama 70B](https://www.youtube.com/watch?v=RYtrF-R5jDc) | 2023-11-27 | [DLExplorer](https://www.youtube.com/@DLExplorers-lg7dt) |
| [How to run open source models on mac Yi 34b on m3 Max](https://www.youtube.com/watch?v=GAo-dopkgjI) | 2023-11-26 | [TECHNO PREMIUM](https://www.youtube.com/@technopremium91) |
| [Yi-34B - 200K - The BEST & NEW CONTEXT WINDOW KING ](https://www.youtube.com/watch?v=7WBojwwv5Qo) | 2023-11-24 | [Prompt Engineering](https://www.youtube.com/@engineerprompt) |
| [Yi 34B : The Rise of Powerful Mid-Sized Models - Base,200k & Chat](https://www.youtube.com/watch?v=bWCjwtu_tHs) | 2023-11-24 | [Sam Witteveen](https://www.youtube.com/@samwitteveenai) |
| [在IoT设备运行破解版李开复大模型dolphin-2.2-yi-34b（还可作为私有OpenAI API服务器）](https://www.bilibili.com/video/BV1SQ4y18744/?spm_id_from=333.337.search-card.all.click&vd_source=ab85f93e294a2f6be11db57c29c6d706) | 2023-11-15 | [Second State](https://github.com/second-state) |
| [Run dolphin-2.2-yi-34b on IoT Devices (Also works as a Private OpenAI API Server)](https://www.youtube.com/watch?v=NJ89T5mO25Y) | 2023-11-14 | [Second State](https://github.com/second-state) |
| [How to Install Yi 34B 200K Llamafied on Windows Laptop](https://www.youtube.com/watch?v=enoha4K4HkQ) | 2023-11-11 | [Fahd Mirza](https://www.youtube.com/@fahdmirza) |
</details>
# Why Yi?
- [Ecosystem](#ecosystem)
- [Upstream](#upstream)
- [Downstream](#downstream)
- [Serving](#serving)
- [Quantization](#quantization-1)
- [Fine-tuning](#fine-tuning-1)
- [API](#api)
- [Benchmarks](#benchmarks)
- [Chat model performance](#chat-model-performance)
- [Base model performance](#base-model-performance)
- [Yi-34B and Yi-34B-200K](#yi-34b-and-yi-34b-200k)
- [Yi-9B](#yi-9b)
## Ecosystem
Yi has a comprehensive ecosystem, offering a range of tools, services, and models to enrich your experiences and maximize productivity.
- [Upstream](#upstream)
- [Downstream](#downstream)
- [Serving](#serving)
- [Quantization](#quantization-1)
- [Fine-tuning](#fine-tuning-1)
- [API](#api)
### Upstream
The Yi series models follow the same model architecture as Llama. By choosing Yi, you can leverage existing tools, libraries, and resources within the Llama ecosystem, eliminating the need to create new tools and enhancing development efficiency.
For example, the Yi series models are saved in the format of the Llama model. You can directly use `LlamaForCausalLM` and `LlamaTokenizer` to load the model. For more information, see [Use the chat model](#31-use-the-chat-model).
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("01-ai/Yi-34b", use_fast=False)
model = AutoModelForCausalLM.from_pretrained("01-ai/Yi-34b", device_map="auto")
```
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### Downstream
> 💡 Tip
>
> - Feel free to create a PR and share the fantastic work you've built using the Yi series models.
>
> - To help others quickly understand your work, it is recommended to use the format of `<model-name>: <model-intro> + <model-highlights>`.
#### Serving
If you want to get up with Yi in a few minutes, you can use the following services built upon Yi.
- Yi-34B-Chat: you can chat with Yi using one of the following platforms:
- [Yi-34B-Chat | Hugging Face](https://huggingface.co/spaces/01-ai/Yi-34B-Chat)
- [Yi-34B-Chat | Yi Platform](https://platform.lingyiwanwu.com/): **Note** that currently it's available through a whitelist. Welcome to apply (fill out a form in [English](https://cn.mikecrm.com/l91ODJf) or [Chinese](https://cn.mikecrm.com/gnEZjiQ)) and experience it firsthand!
- [Yi-6B-Chat (Replicate)](https://replicate.com/01-ai): you can use this model with more options by setting additional parameters and calling APIs.
- [ScaleLLM](https://github.com/vectorch-ai/ScaleLLM#supported-models): you can use this service to run Yi models locally with added flexibility and customization.
#### Quantization
If you have limited computational capabilities, you can use Yi's quantized models as follows.
These quantized models have reduced precision but offer increased efficiency, such as faster inference speed and smaller RAM usage.
- [TheBloke/Yi-34B-GPTQ](https://huggingface.co/TheBloke/Yi-34B-GPTQ)
- [TheBloke/Yi-34B-GGUF](https://huggingface.co/TheBloke/Yi-34B-GGUF)
- [TheBloke/Yi-34B-AWQ](https://huggingface.co/TheBloke/Yi-34B-AWQ)
#### Fine-tuning
If you're seeking to explore the diverse capabilities within Yi's thriving family, you can delve into Yi's fine-tuned models as below.
- [TheBloke Models](https://huggingface.co/TheBloke): this site hosts numerous fine-tuned models derived from various LLMs including Yi.
This is not an exhaustive list for Yi, but to name a few sorted on downloads:
- [TheBloke/dolphin-2_2-yi-34b-AWQ](https://huggingface.co/TheBloke/dolphin-2_2-yi-34b-AWQ)
- [TheBloke/Yi-34B-Chat-AWQ](https://huggingface.co/TheBloke/Yi-34B-Chat-AWQ)
- [TheBloke/Yi-34B-Chat-GPTQ](https://huggingface.co/TheBloke/Yi-34B-Chat-GPTQ)
- [SUSTech/SUS-Chat-34B](https://huggingface.co/SUSTech/SUS-Chat-34B): this model ranked first among all models below 70B and outperformed the twice larger deepseek-llm-67b-chat. You can check the result on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
- [OrionStarAI/OrionStar-Yi-34B-Chat-Llama](https://huggingface.co/OrionStarAI/OrionStar-Yi-34B-Chat-Llama): this model excelled beyond other models (such as GPT-4, Qwen-14B-Chat, Baichuan2-13B-Chat) in C-Eval and CMMLU evaluations on the [OpenCompass LLM Leaderboard](https://opencompass.org.cn/leaderboard-llm).
- [NousResearch/Nous-Capybara-34B](https://huggingface.co/NousResearch/Nous-Capybara-34B): this model is trained with 200K context length and 3 epochs on the Capybara dataset.
#### API
- [amazing-openai-api](https://github.com/soulteary/amazing-openai-api): this tool converts Yi model APIs into the OpenAI API format out of the box.
- [LlamaEdge](https://www.secondstate.io/articles/yi-34b/#create-an-openai-compatible-api-service-for-the-yi-34b-chat-model): this tool builds an OpenAI-compatible API server for Yi-34B-Chat using a portable Wasm (WebAssembly) file, powered by Rust.
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## Tech report
For detailed capabilities of the Yi series model, see [Yi: Open Foundation Models by 01.AI](https://arxiv.org/abs/2403.04652).
### Citation
```
@misc{ai2024yi,
title={Yi: Open Foundation Models by 01.AI},
author={01. AI and : and Alex Young and Bei Chen and Chao Li and Chengen Huang and Ge Zhang and Guanwei Zhang and Heng Li and Jiangcheng Zhu and Jianqun Chen and Jing Chang and Kaidong Yu and Peng Liu and Qiang Liu and Shawn Yue and Senbin Yang and Shiming Yang and Tao Yu and Wen Xie and Wenhao Huang and Xiaohui Hu and Xiaoyi Ren and Xinyao Niu and Pengcheng Nie and Yuchi Xu and Yudong Liu and Yue Wang and Yuxuan Cai and Zhenyu Gu and Zhiyuan Liu and Zonghong Dai},
year={2024},
eprint={2403.04652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Benchmarks
- [Chat model performance](#chat-model-performance)
- [Base model performance](#base-model-performance)
### Chat model performance
Yi-34B-Chat model demonstrates exceptional performance, ranking first among all existing open-source models in the benchmarks including MMLU, CMMLU, BBH, GSM8k, and more.
<details>
<summary> Evaluation methods and challenges. ⬇️ </summary>
- **Evaluation methods**: we evaluated various benchmarks using both zero-shot and few-shot methods, except for TruthfulQA.
- **Zero-shot vs. few-shot**: in chat models, the zero-shot approach is more commonly employed.
- **Evaluation strategy**: our evaluation strategy involves generating responses while following instructions explicitly or implicitly (such as using few-shot examples). We then isolate relevant answers from the generated text.
- **Challenges faced**: some models are not well-suited to produce output in the specific format required by instructions in few datasets, which leads to suboptimal results.
<strong>*</strong>: C-Eval results are evaluated on the validation datasets
</details>
### Base model performance
#### Yi-34B and Yi-34B-200K
The Yi-34B and Yi-34B-200K models stand out as the top performers among open-source models, especially excelling in MMLU, CMMLU, common-sense reasoning, reading comprehension, and more.
<details>
<summary> Evaluation methods. ⬇️</summary>
- **Disparity in results**: while benchmarking open-source models, a disparity has been noted between results from our pipeline and those reported by public sources like OpenCompass.
- **Investigation findings**: a deeper investigation reveals that variations in prompts, post-processing strategies, and sampling techniques across models may lead to significant outcome differences.
- **Uniform benchmarking process**: our methodology aligns with the original benchmarks—consistent prompts and post-processing strategies are used, and greedy decoding is applied during evaluations without any post-processing for the generated content.
- **Efforts to retrieve unreported scores**: for scores that were not reported by the original authors (including scores reported with different settings), we try to get results with our pipeline.
- **Extensive model evaluation**: to evaluate the model’s capability extensively, we adopted the methodology outlined in Llama2. Specifically, we included PIQA, SIQA, HellaSwag, WinoGrande, ARC, OBQA, and CSQA to assess common sense reasoning. SquAD, QuAC, and BoolQ were incorporated to evaluate reading comprehension.
- **Special configurations**: CSQA was exclusively tested using a 7-shot setup, while all other tests were conducted with a 0-shot configuration. Additionally, we introduced GSM8K (8-shot@1), MATH (4-shot@1), HumanEval (0-shot@1), and MBPP (3-shot@1) under the category "Math & Code".
- **Falcon-180B caveat**: Falcon-180B was not tested on QuAC and OBQA due to technical constraints. Its performance score is an average from other tasks, and considering the generally lower scores of these two tasks, Falcon-180B's capabilities are likely not underestimated.
</details>
#### Yi-9B
Yi-9B is almost the best among a range of similar-sized open-source models (including Mistral-7B, SOLAR-10.7B, Gemma-7B, DeepSeek-Coder-7B-Base-v1.5 and more), particularly excelling in code, math, common-sense reasoning, and reading comprehension.
- In terms of **overall** ability (Mean-All), Yi-9B performs the best among similarly sized open-source models, surpassing DeepSeek-Coder, DeepSeek-Math, Mistral-7B, SOLAR-10.7B, and Gemma-7B.
- In terms of **coding** ability (Mean-Code), Yi-9B's performance is second only to DeepSeek-Coder-7B, surpassing Yi-34B, SOLAR-10.7B, Mistral-7B, and Gemma-7B.
- In terms of **math** ability (Mean-Math), Yi-9B's performance is second only to DeepSeek-Math-7B, surpassing SOLAR-10.7B, Mistral-7B, and Gemma-7B.
- In terms of **common sense and reasoning** ability (Mean-Text), Yi-9B's performance is on par with Mistral-7B, SOLAR-10.7B, and Gemma-7B.
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# Who can use Yi?
Everyone! 🙌 ✅
The code and weights of the Yi series models are distributed under the [Apache 2.0 license](https://github.com/01-ai/Yi/blob/main/LICENSE), which means the Yi series models are free for personal usage, academic purposes, and commercial use.
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# Misc.
### Acknowledgments
A heartfelt thank you to each of you who have made contributions to the Yi community! You have helped Yi not just a project, but a vibrant, growing home for innovation.
[](https://github.com/01-ai/yi/graphs/contributors)
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### Disclaimer
We use data compliance checking algorithms during the training process, to
ensure the compliance of the trained model to the best of our ability. Due to
complex data and the diversity of language model usage scenarios, we cannot
guarantee that the model will generate correct, and reasonable output in all
scenarios. Please be aware that there is still a risk of the model producing
problematic outputs. We will not be responsible for any risks and issues
resulting from misuse, misguidance, illegal usage, and related misinformation,
as well as any associated data security concerns.
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### License
The code and weights of the Yi-1.5 series models are distributed under the [Apache 2.0 license](https://github.com/01-ai/Yi/blob/main/LICENSE).
If you create derivative works based on this model, please include the following attribution in your derivative works:
This work is a derivative of [The Yi Series Model You Base On] by 01.AI, used under the Apache 2.0 License.
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|
kavlab/distilbert-base-uncased-finetuned
|
kavlab
| 2024-11-10T17:01:04Z | 105 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-10T14:37:46Z |
---
base_model: distilbert/distilbert-base-uncased
library_name: transformers
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7551
- Accuracy: 0.8719
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5931 | 1.0 | 2058 | 0.5446 | 0.7920 |
| 0.4026 | 2.0 | 4116 | 0.4697 | 0.8343 |
| 0.2937 | 3.0 | 6174 | 0.4141 | 0.8687 |
| 0.2244 | 4.0 | 8232 | 0.4580 | 0.8695 |
| 0.1796 | 5.0 | 10290 | 0.5344 | 0.8659 |
| 0.1478 | 6.0 | 12348 | 0.5953 | 0.8702 |
| 0.114 | 7.0 | 14406 | 0.6643 | 0.8690 |
| 0.0949 | 8.0 | 16464 | 0.7232 | 0.8641 |
| 0.0672 | 9.0 | 18522 | 0.7597 | 0.8678 |
| 0.0511 | 10.0 | 20580 | 0.7551 | 0.8719 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.1.1
- Datasets 2.12.0
- Tokenizers 0.20.1
|
gavinqiangli/my-awesome-cross-encoder
|
gavinqiangli
| 2024-11-10T16:43:05Z | 105 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"cross-encoder",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-10T16:42:47Z |
---
library_name: transformers
tags:
- cross-encoder
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
yejinkim/forget10_expert_epoch7
|
yejinkim
| 2024-11-10T16:26:46Z | 135 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-10T16:20:03Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
dmabby/bert2-finetuned-ner
|
dmabby
| 2024-11-10T16:26:18Z | 63 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-11-10T15:51:54Z |
---
library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_keras_callback
model-index:
- name: dmabby/bert2-finetuned-ner
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# dmabby/bert2-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3737
- Validation Loss: 0.3562
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 21, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.4645 | 0.3562 | 0 |
| 0.3770 | 0.3562 | 1 |
| 0.3737 | 0.3562 | 2 |
### Framework versions
- Transformers 4.45.1
- TensorFlow 2.17.0
- Datasets 3.1.0
- Tokenizers 0.20.0
|
harshvardhanj733/results_english
|
harshvardhanj733
| 2024-11-10T16:19:49Z | 180 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-10T16:18:27Z |
---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: results_english
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results_english
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7851
- Accuracy: 0.7178
- Precision: 0.7201
- Recall: 0.7178
- F1: 0.7182
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 1.0 | 264 | 0.8362 | 0.6667 | 0.6659 | 0.6667 | 0.6634 |
| 0.9341 | 2.0 | 528 | 0.7913 | 0.6856 | 0.6901 | 0.6856 | 0.6794 |
| 0.9341 | 3.0 | 792 | 0.7716 | 0.6951 | 0.6974 | 0.6951 | 0.6919 |
| 0.6719 | 4.0 | 1056 | 0.8301 | 0.7159 | 0.7185 | 0.7159 | 0.7163 |
| 0.6719 | 5.0 | 1320 | 0.7851 | 0.7178 | 0.7201 | 0.7178 | 0.7182 |
| 0.5313 | 6.0 | 1584 | 0.9683 | 0.6761 | 0.6809 | 0.6761 | 0.6698 |
| 0.5313 | 7.0 | 1848 | 1.1330 | 0.6913 | 0.6923 | 0.6913 | 0.6883 |
| 0.4155 | 8.0 | 2112 | 1.2025 | 0.7102 | 0.7094 | 0.7102 | 0.7084 |
| 0.4155 | 9.0 | 2376 | 1.5090 | 0.6686 | 0.6711 | 0.6686 | 0.6595 |
| 0.3457 | 10.0 | 2640 | 1.6342 | 0.6856 | 0.6871 | 0.6856 | 0.6847 |
| 0.3457 | 11.0 | 2904 | 1.7451 | 0.6875 | 0.6923 | 0.6875 | 0.6879 |
| 0.3272 | 12.0 | 3168 | 1.8827 | 0.7027 | 0.7017 | 0.7027 | 0.6991 |
| 0.3272 | 13.0 | 3432 | 1.9303 | 0.6875 | 0.6868 | 0.6875 | 0.6865 |
| 0.2553 | 14.0 | 3696 | 1.9490 | 0.6913 | 0.6897 | 0.6913 | 0.6895 |
| 0.2553 | 15.0 | 3960 | 1.9609 | 0.6913 | 0.6902 | 0.6913 | 0.6895 |
| 0.2349 | 16.0 | 4224 | 1.9921 | 0.6875 | 0.6850 | 0.6875 | 0.6848 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1
|
SufficientPrune3897/magnum-v4-123b-exl2-2.65bpw
|
SufficientPrune3897
| 2024-11-10T16:18:46Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"chat",
"conversational",
"en",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
] |
text-generation
| 2024-11-10T14:04:02Z |
---
license: other
license_name: mrl
language:
- en
tags:
- chat
pipeline_tag: text-generation
library_name: transformers
---
Quant of:
This is a series of models designed to replicate the prose quality of the Claude 3 models, specifically Sonnet and Opus.
This model is fine-tuned on top of [mistralai/Mistral-Large-Instruct-2407](https://huggingface.co/mistralai/Mistral-Large-Instruct-2407).
## Prompting
A typical input would look like this:
```py
<s>[INST] SYSTEM MESSAGE\nUSER MESSAGE[/INST] ASSISTANT MESSAGE</s>[INST] USER MESSAGE[/INST]
```
## SillyTavern templates
Below are Instruct and Context templates for use within SillyTavern.
<details><summary>context template</summary>
```yaml
default SillyTavern template works fine
```
</details><br>
<details><summary>instruct template</summary>
```yaml
default SillyTavern template works fine
```
</details><br>
## Axolotl config
<details><summary>See axolotl config</summary>
```yaml
base_model: mistralai/Mistral-Large-Instruct-2407
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
plugins:
- axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_swiglu: true
liger_fused_linear_cross_entropy: true
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: anthracite-org/c2_logs_16k_mistral-large_v1.2
type: sharegpt
conversation: mistral
- path: anthracite-org/kalo-opus-instruct-22k-no-refusal
type: sharegpt
conversation: mistral
- path: lodrick-the-lafted/kalo-opus-instruct-3k-filtered
type: sharegpt
conversation: mistral
- path: anthracite-org/nopm_claude_writing_fixed
type: sharegpt
conversation: mistral
- path: anthracite-org/kalo_opus_misc_240827
type: sharegpt
conversation: mistral
- path: anthracite-org/kalo_misc_part2
type: sharegpt
conversation: mistral
#chat_template: chatml
shuffle_merged_datasets: true
#default_system_message: "You are an assistant that responds to the user."
dataset_prepared_path: ./data/magnum-123b-data
val_set_size: 0.0
output_dir: ./data/123b-fft-out
sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true
adapter:
lora_model_dir:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
lora_fan_in_fan_out:
wandb_project: 123b-magnum-fft
wandb_entity:
wandb_watch:
wandb_name: alter-attempt-04
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0000015
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: unsloth
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 40
evals_per_epoch:
eval_table_size:
eval_max_new_tokens:
saves_per_epoch: 2
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.01
fsdp:
fsdp_config:
special_tokens:
```
</details><br>
## Credits
We'd like to thank [Eric Hartford](https://huggingface.co/ehartford) for sponsoring the compute for this train.
We would also like to thank all members of Anthracite who made this finetune possible.
## Datasets
- [anthracite-org/c2_logs_16k_mistral-large_v1.2](https://huggingface.co/datasets/anthracite-org/c2_logs_16k_mistral-large_v1.2)
- [anthracite-org/kalo-opus-instruct-22k-no-refusal](https://huggingface.co/datasets/anthracite-org/kalo-opus-instruct-22k-no-refusal)
- [lodrick-the-lafted/kalo-opus-instruct-3k-filtered](https://huggingface.co/datasets/lodrick-the-lafted/kalo-opus-instruct-3k-filtered)
- [anthracite-org/nopm_claude_writing_fixed](https://huggingface.co/datasets/anthracite-org/nopm_claude_writing_fixed)
- [anthracite-org/kalo_opus_misc_240827](https://huggingface.co/datasets/anthracite-org/kalo_opus_misc_240827)
- [anthracite-org/kalo_misc_part2](https://huggingface.co/datasets/anthracite-org/kalo_misc_part2)
## Training
We used 8x mi300x GPUs graciously provided by [Eric Hartford](https://huggingface.co/ehartford) for the full-parameter fine-tuning of the model.
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
## Safety
...
|
amin1123/whisper-small-ps
|
amin1123
| 2024-11-10T16:15:28Z | 77 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"ps",
"dataset:pairsys/open_asr",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-11-10T04:58:42Z |
---
library_name: transformers
language:
- ps
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- pairsys/open_asr
metrics:
- wer
model-index:
- name: Whisper Small Pashto
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Open ASR
type: pairsys/open_asr
args: 'config: pashto'
metrics:
- name: Wer
type: wer
value: 34.475374732334046
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Pashto
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Open ASR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7846
- Wer: 34.4754
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.0112 | 17.8571 | 1000 | 0.6265 | 38.1462 |
| 0.0023 | 35.7143 | 2000 | 0.7230 | 35.0260 |
| 0.0006 | 53.5714 | 3000 | 0.7555 | 34.7201 |
| 0.0001 | 71.4286 | 4000 | 0.7708 | 34.9342 |
| 0.0001 | 89.2857 | 5000 | 0.7846 | 34.4754 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.3
|
ihughes15234/phi35_tictactoe_dpo1epoch_v3
|
ihughes15234
| 2024-11-10T16:10:45Z | 77 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:ihughes15234/phi35_tictactoe_dpo6epoch_v2",
"base_model:finetune:ihughes15234/phi35_tictactoe_dpo6epoch_v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-10T16:07:21Z |
---
base_model: ihughes15234/phi35_tictactoe_dpo6epoch_v2
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** ihughes15234
- **License:** apache-2.0
- **Finetuned from model :** ihughes15234/phi35_tictactoe_dpo6epoch_v2
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
PopularPenguin/t5-small-awesome-text-to-sql-2024-11-10_13-40
|
PopularPenguin
| 2024-11-10T15:57:55Z | 45 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:arrow",
"base_model:cssupport/t5-small-awesome-text-to-sql",
"base_model:finetune:cssupport/t5-small-awesome-text-to-sql",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-11-10T13:42:52Z |
---
library_name: transformers
license: apache-2.0
base_model: cssupport/t5-small-awesome-text-to-sql
tags:
- generated_from_trainer
datasets:
- arrow
model-index:
- name: t5-small-awesome-text-to-sql-2024-11-10_13-40
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-awesome-text-to-sql-2024-11-10_13-40
This model is a fine-tuned version of [cssupport/t5-small-awesome-text-to-sql](https://huggingface.co/cssupport/t5-small-awesome-text-to-sql) on the arrow dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1505
- Gen Len: 19.0
- Bertscorer-p: 0.5983
- Bertscorer-r: 0.1002
- Bertscorer-f1: 0.3375
- Sacrebleu-score: 6.1735
- Sacrebleu-precisions: [92.82196987876635, 86.09309987961223, 81.16865589315682, 77.5936294965929]
- Bleu-bp: 0.0733
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Gen Len | Bertscorer-p | Bertscorer-r | Bertscorer-f1 | Sacrebleu-score | Sacrebleu-precisions | Bleu-bp |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------------:|:------------:|:-------------:|:---------------:|:----------------------------------------------------------------------------:|:-------:|
| 0.2655 | 1.0 | 4772 | 0.2099 | 19.0 | 0.5770 | 0.0864 | 0.3203 | 5.7173 | [91.0934769807022, 81.88030009989161, 75.59001146341751, 71.32247244849066] | 0.0718 |
| 0.1951 | 2.0 | 9544 | 0.1772 | 19.0 | 0.5695 | 0.0718 | 0.3090 | 5.7315 | [91.38097911302968, 82.52214039836731, 76.55664627495614, 73.06145893164847] | 0.0711 |
| 0.1609 | 3.0 | 14316 | 0.1628 | 19.0 | 0.5960 | 0.1033 | 0.3382 | 6.0737 | [92.32304047118862, 84.75338215740487, 79.32502315982035, 75.25860249102807] | 0.0735 |
| 0.1412 | 4.0 | 19088 | 0.1551 | 19.0 | 0.5925 | 0.0959 | 0.3326 | 6.0701 | [92.56176903043524, 85.09918369073299, 79.79597353297214, 76.12497023888257] | 0.0730 |
| 0.1191 | 5.0 | 23860 | 0.1512 | 19.0 | 0.5905 | 0.0928 | 0.3300 | 6.0937 | [92.29263048778147, 84.9906547977318, 79.83711978971085, 76.22241882452364] | 0.0733 |
| 0.1063 | 6.0 | 28632 | 0.1486 | 19.0 | 0.5959 | 0.0986 | 0.3356 | 6.1128 | [92.67271190348113, 85.5578689269597, 80.37916696032137, 76.71086200742904] | 0.0731 |
| 0.094 | 7.0 | 33404 | 0.1489 | 19.0 | 0.5984 | 0.1024 | 0.3388 | 6.1770 | [92.60841659561831, 85.6159908960634, 80.52775143703391, 76.7429609924408] | 0.0738 |
| 0.0875 | 8.0 | 38176 | 0.1496 | 19.0 | 0.5960 | 0.0976 | 0.3351 | 6.1421 | [92.6290822842547, 85.75971432797346, 80.81931219105543, 77.24221764177369] | 0.0732 |
| 0.0841 | 9.0 | 42948 | 0.1498 | 19.0 | 0.6019 | 0.1059 | 0.3424 | 6.2261 | [92.84100049795074, 86.14431816984929, 81.20480235905357, 77.4564647967041] | 0.0739 |
| 0.0777 | 10.0 | 47720 | 0.1505 | 19.0 | 0.5983 | 0.1002 | 0.3375 | 6.1735 | [92.82196987876635, 86.09309987961223, 81.16865589315682, 77.5936294965929] | 0.0733 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
ICT3214-Group5/MD5_gpt_neo_v1.1.3
|
ICT3214-Group5
| 2024-11-10T15:56:44Z | 116 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"base_model:EleutherAI/gpt-neo-125m",
"base_model:finetune:EleutherAI/gpt-neo-125m",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-10T15:01:29Z |
---
library_name: transformers
license: mit
base_model: EleutherAI/gpt-neo-125M
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: MD5_gpt_neo_v1.1.3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MD5_gpt_neo_v1.1.3
This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0538
- Rouge1: 0.5076
- Rouge2: 0.2548
- Rougel: 0.4743
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| No log | 1.0 | 70 | 0.0628 | 0.4870 | 0.2269 | 0.4475 |
| No log | 2.0 | 140 | 0.0566 | 0.4913 | 0.2367 | 0.4607 |
| No log | 3.0 | 210 | 0.0545 | 0.4972 | 0.2484 | 0.4667 |
| No log | 4.0 | 280 | 0.0544 | 0.5023 | 0.2586 | 0.4749 |
| No log | 5.0 | 350 | 0.0538 | 0.5076 | 0.2548 | 0.4743 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.1
|
mradermacher/Pocky_9B-GGUF
|
mradermacher
| 2024-11-10T15:50:09Z | 5 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:ChaoticNeutrals/Pocky_9B",
"base_model:quantized:ChaoticNeutrals/Pocky_9B",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-11-08T20:28:22Z |
---
base_model: ChaoticNeutrals/Pocky_9B
language:
- en
library_name: transformers
license: other
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/ChaoticNeutrals/Pocky_9B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Pocky_9B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Pocky_9B-GGUF/resolve/main/Pocky_9B.Q2_K.gguf) | Q2_K | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/Pocky_9B-GGUF/resolve/main/Pocky_9B.Q3_K_S.gguf) | Q3_K_S | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Pocky_9B-GGUF/resolve/main/Pocky_9B.Q3_K_M.gguf) | Q3_K_M | 4.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Pocky_9B-GGUF/resolve/main/Pocky_9B.Q3_K_L.gguf) | Q3_K_L | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/Pocky_9B-GGUF/resolve/main/Pocky_9B.IQ4_XS.gguf) | IQ4_XS | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/Pocky_9B-GGUF/resolve/main/Pocky_9B.Q4_0_4_4.gguf) | Q4_0_4_4 | 5.2 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Pocky_9B-GGUF/resolve/main/Pocky_9B.Q4_K_S.gguf) | Q4_K_S | 5.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Pocky_9B-GGUF/resolve/main/Pocky_9B.Q4_K_M.gguf) | Q4_K_M | 5.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Pocky_9B-GGUF/resolve/main/Pocky_9B.Q5_K_S.gguf) | Q5_K_S | 6.3 | |
| [GGUF](https://huggingface.co/mradermacher/Pocky_9B-GGUF/resolve/main/Pocky_9B.Q5_K_M.gguf) | Q5_K_M | 6.5 | |
| [GGUF](https://huggingface.co/mradermacher/Pocky_9B-GGUF/resolve/main/Pocky_9B.Q6_K.gguf) | Q6_K | 7.5 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Pocky_9B-GGUF/resolve/main/Pocky_9B.Q8_0.gguf) | Q8_0 | 9.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Pocky_9B-GGUF/resolve/main/Pocky_9B.f16.gguf) | f16 | 18.1 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
KienT/sd-class-butterflies-32
|
KienT
| 2024-11-10T15:45:50Z | 47 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2024-11-10T15:45:31Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('KienT/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
wsklee/distilbert-sentiment-cpt-v2
|
wsklee
| 2024-11-10T15:45:33Z | 197 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"fill-mask",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2024-11-10T14:31:00Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-sentiment-cpt-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-sentiment-cpt-v2
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1448
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 128
- eval_batch_size: 256
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.1641 | 5.5471 | 1000 | 2.1448 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.3
|
Keltezaa/neon-environments
|
Keltezaa
| 2024-11-10T15:40:59Z | 73 | 4 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"migrated",
"3d",
"bar",
"background",
"arcade",
"living room",
"train",
"bathroom",
"bowling",
"diner",
"pub",
"hallway",
"backgrounds",
"neons",
"escalator",
"disco bar",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2024-10-25T13:00:26Z |
---
license: other
license_name: bespoke-lora-trained-license
license_link: >-
https://multimodal.art/civitai-licenses?allowNoCredit=False&allowCommercialUse=RentCivit&allowDerivatives=False&allowDifferentLicense=True
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
- migrated
- 3d
- bar
- background
- arcade
- living room
- train
- bathroom
- bowling
- diner
- pub
- hallway
- backgrounds
- neons
- escalator
- disco bar
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: null
widget:
- text: ' '
output:
url: 31343274.jpeg
- text: ' '
output:
url: 31343350.jpeg
- text: ' '
output:
url: 31343277.jpeg
- text: ' '
output:
url: 31343276.jpeg
- text: ' '
output:
url: 31343637.jpeg
- text: ' '
output:
url: 31343886.jpeg
- text: ' '
output:
url: 31343254.jpeg
- text: ' '
output:
url: 31343255.jpeg
- text: ' '
output:
url: 31343256.jpeg
- text: A pornstar woman holding a Neon sign "SLDR Flux NSFW v2 Studio"
output:
url: images/example_sfeoq5az5.png
- text: a Neon sign that show an illustration of (2 fingers and a pussy)
output:
url: images/example_hrthffo2b.png
---
# Neon Environments
<Gallery />
([CivitAI](https://civitai.com/models/))
## Model description
<p>Introducing Neon Environments Model: Illuminating Arcades and Pubs</p><p>Neon Environments Model, is designed to generate visually striking images inspired by arcades, pubs, and other premises adorned with neon lights.</p>
## Download model
Weights for this model are available in Safetensors format.
[Download](/Keltezaa/neon-environments/tree/main) them in the Files & versions tab.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to(device)
pipeline.load_lora_weights('Keltezaa/neon-environments', weight_name='Neon_Environments.safetensors')
image = pipeline('Your custom prompt').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
|
theprint/ReWiz-Llama-3.1-8B-v2
|
theprint
| 2024-11-10T15:33:52Z | 191 | 1 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"alpaca",
"rewiz",
"en",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-02T09:52:58Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
- alpaca
- rewiz
base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
model-index:
- name: ReWiz-Llama-3.1-8B-v2
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 23.73
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=theprint/ReWiz-Llama-3.1-8B-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 23.77
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=theprint/ReWiz-Llama-3.1-8B-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 4.53
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=theprint/ReWiz-Llama-3.1-8B-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 7.05
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=theprint/ReWiz-Llama-3.1-8B-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 9.34
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=theprint/ReWiz-Llama-3.1-8B-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 25.67
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=theprint/ReWiz-Llama-3.1-8B-v2
name: Open LLM Leaderboard
---
<img src="https://huggingface.co/theprint/ReWiz-Llama-3.2-3B/resolve/main/ReWiz_banner.png">
# Prompt format
Use an alpaca-style prompt template for best results.
# Uploaded model
- **Developed by:** theprint
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_theprint__ReWiz-Llama-3.1-8B-v2)
| Metric |Value|
|-------------------|----:|
|Avg. |15.68|
|IFEval (0-Shot) |23.73|
|BBH (3-Shot) |23.77|
|MATH Lvl 5 (4-Shot)| 4.53|
|GPQA (0-shot) | 7.05|
|MuSR (0-shot) | 9.34|
|MMLU-PRO (5-shot) |25.67|
|
theprint/ReWiz-Nemo-12B-Instruct
|
theprint
| 2024-11-10T15:32:29Z | 8 | 2 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit",
"base_model:finetune:unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-31T02:01:46Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
- sft
base_model: unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit
model-index:
- name: ReWiz-Nemo-12B-Instruct
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 10.62
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=theprint/ReWiz-Nemo-12B-Instruct
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 29.93
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=theprint/ReWiz-Nemo-12B-Instruct
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 7.18
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=theprint/ReWiz-Nemo-12B-Instruct
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 9.84
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=theprint/ReWiz-Nemo-12B-Instruct
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 10.23
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=theprint/ReWiz-Nemo-12B-Instruct
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 25.99
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=theprint/ReWiz-Nemo-12B-Instruct
name: Open LLM Leaderboard
---
<img src="https://huggingface.co/theprint/ReWiz-Llama-3.2-3B/resolve/main/ReWiz_banner.png">
Half the data was geared towards better reasoning (EvolKit-20k and reasoning-base-20k), the other half will help to de-censor the model (WizardLM data set).
# Looking for GGUF?
There is a separate upload for that! Download [theprint/ReWiz-Nemo-12B-Instruct-GGUF](https://huggingface.co/theprint/ReWiz-Nemo-12B-Instruct-GGUF) instead.
# Uploaded model
- **Developed by:** theprint
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_theprint__ReWiz-Nemo-12B-Instruct)
| Metric |Value|
|-------------------|----:|
|Avg. |15.63|
|IFEval (0-Shot) |10.62|
|BBH (3-Shot) |29.93|
|MATH Lvl 5 (4-Shot)| 7.18|
|GPQA (0-shot) | 9.84|
|MuSR (0-shot) |10.23|
|MMLU-PRO (5-shot) |25.99|
|
am0n666x/aya-expanse-8b-ungated-Q4_K_S-GGUF
|
am0n666x
| 2024-11-10T15:31:17Z | 5 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"fr",
"de",
"es",
"it",
"pt",
"ja",
"ko",
"zh",
"ar",
"el",
"fa",
"pl",
"id",
"cs",
"he",
"hi",
"nl",
"ro",
"ru",
"tr",
"uk",
"vi",
"base_model:adamo1139/aya-expanse-8b-ungated",
"base_model:quantized:adamo1139/aya-expanse-8b-ungated",
"license:cc-by-nc-4.0",
"region:us",
"conversational"
] | null | 2024-11-10T15:30:54Z |
---
inference: false
library_name: transformers
language:
- en
- fr
- de
- es
- it
- pt
- ja
- ko
- zh
- ar
- el
- fa
- pl
- id
- cs
- he
- hi
- nl
- ro
- ru
- tr
- uk
- vi
license: cc-by-nc-4.0
base_model: adamo1139/aya-expanse-8b-ungated
tags:
- llama-cpp
- gguf-my-repo
---
# am0n666x/aya-expanse-8b-ungated-Q4_K_S-GGUF
This model was converted to GGUF format from [`adamo1139/aya-expanse-8b-ungated`](https://huggingface.co/adamo1139/aya-expanse-8b-ungated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/adamo1139/aya-expanse-8b-ungated) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo am0n666x/aya-expanse-8b-ungated-Q4_K_S-GGUF --hf-file aya-expanse-8b-ungated-q4_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo am0n666x/aya-expanse-8b-ungated-Q4_K_S-GGUF --hf-file aya-expanse-8b-ungated-q4_k_s.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo am0n666x/aya-expanse-8b-ungated-Q4_K_S-GGUF --hf-file aya-expanse-8b-ungated-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo am0n666x/aya-expanse-8b-ungated-Q4_K_S-GGUF --hf-file aya-expanse-8b-ungated-q4_k_s.gguf -c 2048
```
|
Seyfelislem/afrispeech_large_A100
|
Seyfelislem
| 2024-11-10T15:26:56Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:afrispeech-200",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-04-03T21:27:00Z |
---
tags:
- generated_from_trainer
datasets:
- afrispeech-200
metrics:
- wer
model-index:
- name: afrispeech_large_A100
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: afrispeech-200
type: afrispeech-200
config: all
split: train
args: all
metrics:
- name: Wer
type: wer
value: 14.81
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# afrispeech_large_A100
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the afrispeech-200 dataset.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP
### Training results
https://huggingface.co/Seyfelislem/afrispeech_large_A100/tensorboard
### Framework versions
- Transformers 4.29.1
- Pytorch 1.13.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
RichardErkhov/hfl_-_chinese-mixtral-gguf
|
RichardErkhov
| 2024-11-10T15:21:14Z | 8 | 0 | null |
[
"gguf",
"arxiv:2403.01851",
"endpoints_compatible",
"region:us"
] | null | 2024-11-10T03:36:34Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
chinese-mixtral - GGUF
- Model creator: https://huggingface.co/hfl/
- Original model: https://huggingface.co/hfl/chinese-mixtral/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [chinese-mixtral.Q2_K.gguf](https://huggingface.co/RichardErkhov/hfl_-_chinese-mixtral-gguf/blob/main/chinese-mixtral.Q2_K.gguf) | Q2_K | 16.12GB |
| [chinese-mixtral.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/hfl_-_chinese-mixtral-gguf/blob/main/chinese-mixtral.Q3_K_S.gguf) | Q3_K_S | 19.03GB |
| [chinese-mixtral.Q3_K.gguf](https://huggingface.co/RichardErkhov/hfl_-_chinese-mixtral-gguf/blob/main/chinese-mixtral.Q3_K.gguf) | Q3_K | 21.0GB |
| [chinese-mixtral.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/hfl_-_chinese-mixtral-gguf/blob/main/chinese-mixtral.Q3_K_M.gguf) | Q3_K_M | 21.0GB |
| [chinese-mixtral.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/hfl_-_chinese-mixtral-gguf/blob/main/chinese-mixtral.Q3_K_L.gguf) | Q3_K_L | 22.51GB |
| [chinese-mixtral.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/hfl_-_chinese-mixtral-gguf/blob/main/chinese-mixtral.IQ4_XS.gguf) | IQ4_XS | 23.63GB |
| [chinese-mixtral.Q4_0.gguf](https://huggingface.co/RichardErkhov/hfl_-_chinese-mixtral-gguf/blob/main/chinese-mixtral.Q4_0.gguf) | Q4_0 | 24.63GB |
| [chinese-mixtral.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/hfl_-_chinese-mixtral-gguf/blob/main/chinese-mixtral.IQ4_NL.gguf) | IQ4_NL | 24.91GB |
| [chinese-mixtral.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/hfl_-_chinese-mixtral-gguf/blob/main/chinese-mixtral.Q4_K_S.gguf) | Q4_K_S | 24.91GB |
| [chinese-mixtral.Q4_K.gguf](https://huggingface.co/RichardErkhov/hfl_-_chinese-mixtral-gguf/blob/main/chinese-mixtral.Q4_K.gguf) | Q4_K | 26.49GB |
| [chinese-mixtral.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/hfl_-_chinese-mixtral-gguf/blob/main/chinese-mixtral.Q4_K_M.gguf) | Q4_K_M | 26.49GB |
| [chinese-mixtral.Q4_1.gguf](https://huggingface.co/RichardErkhov/hfl_-_chinese-mixtral-gguf/blob/main/chinese-mixtral.Q4_1.gguf) | Q4_1 | 27.32GB |
| [chinese-mixtral.Q5_0.gguf](https://huggingface.co/RichardErkhov/hfl_-_chinese-mixtral-gguf/blob/main/chinese-mixtral.Q5_0.gguf) | Q5_0 | 30.02GB |
| [chinese-mixtral.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/hfl_-_chinese-mixtral-gguf/blob/main/chinese-mixtral.Q5_K_S.gguf) | Q5_K_S | 30.02GB |
| [chinese-mixtral.Q5_K.gguf](https://huggingface.co/RichardErkhov/hfl_-_chinese-mixtral-gguf/blob/main/chinese-mixtral.Q5_K.gguf) | Q5_K | 30.95GB |
| [chinese-mixtral.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/hfl_-_chinese-mixtral-gguf/blob/main/chinese-mixtral.Q5_K_M.gguf) | Q5_K_M | 30.95GB |
| [chinese-mixtral.Q5_1.gguf](https://huggingface.co/RichardErkhov/hfl_-_chinese-mixtral-gguf/blob/main/chinese-mixtral.Q5_1.gguf) | Q5_1 | 32.71GB |
| [chinese-mixtral.Q6_K.gguf](https://huggingface.co/RichardErkhov/hfl_-_chinese-mixtral-gguf/blob/main/chinese-mixtral.Q6_K.gguf) | Q6_K | 35.74GB |
| [chinese-mixtral.Q8_0.gguf](https://huggingface.co/RichardErkhov/hfl_-_chinese-mixtral-gguf/tree/main/) | Q8_0 | 46.22GB |
Original model description:
---
license: apache-2.0
language:
- zh
- en
tags:
- moe
---
# Chinese-Mixtral
<p align="center">
<a href="https://github.com/ymcui/Chinese-Mixtral"><img src="https://ymcui.com/images/chinese-mixtral-banner.png" width="600"/></a>
</p>
**Chinese Mixtral GitHub repository: https://github.com/ymcui/Chinese-Mixtral**
This repository contains **Chinese-Mixtral**, which is further pre-trained on [Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1).
**Note: this is a foundation model, which is not suitable for conversation, QA, etc.**
## Others
- For LoRA-only model, please see: https://huggingface.co/hfl/chinese-mixtral-lora
- For GGUF model (llama.cpp compatible), please see: https://huggingface.co/hfl/chinese-mixtral-gguf
- If you have questions/issues regarding this model, please submit an issue through https://github.com/ymcui/Chinese-Mixtral/.
## Citation
Please consider cite our paper if you use the resource of this repository.
Paper link: https://arxiv.org/abs/2403.01851
```
@article{chinese-mixtral,
title={Rethinking LLM Language Adaptation: A Case Study on Chinese Mixtral},
author={Cui, Yiming and Yao, Xin},
journal={arXiv preprint arXiv:2403.01851},
url={https://arxiv.org/abs/2403.01851},
year={2024}
}
```
|
yoginim/northcoste
|
yoginim
| 2024-11-10T15:10:55Z | 37 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-10T15:01:35Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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## Glossary [optional]
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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Filtered Qwen2.5 Distill Models
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Filtered Distill Qwen 7B Models
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Filtered Qwen-7b Model Cards
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Filtered Qwen 7B Model Cards
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Qwen 7B Distilled Models
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Qwen 7B Distilled Model Cards
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Qwen 7B Distilled Models
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Distilled Qwen 7B Models
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Distill Qwen 7B Model Cards
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