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2025-07-15 00:43:56
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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/amd-Meta-Llama-3-8B_fp8_quark-GGUF | featherless-ai-quants | 2024-11-10T19:38:01Z | 5 | 0 | null | [
"gguf",
"text-generation",
"base_model:amd/Meta-Llama-3-8B_fp8_quark",
"base_model:quantized:amd/Meta-Llama-3-8B_fp8_quark",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-11-05T03:47:06Z | ---
base_model: amd/Meta-Llama-3-8B_fp8_quark
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# amd/Meta-Llama-3-8B_fp8_quark 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 | [amd-Meta-Llama-3-8B_fp8_quark-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/amd-Meta-Llama-3-8B_fp8_quark-GGUF/blob/main/amd-Meta-Llama-3-8B_fp8_quark-IQ4_XS.gguf) | 4276.62 MB |
| Q2_K | [amd-Meta-Llama-3-8B_fp8_quark-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/amd-Meta-Llama-3-8B_fp8_quark-GGUF/blob/main/amd-Meta-Llama-3-8B_fp8_quark-Q2_K.gguf) | 3031.86 MB |
| Q3_K_L | [amd-Meta-Llama-3-8B_fp8_quark-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/amd-Meta-Llama-3-8B_fp8_quark-GGUF/blob/main/amd-Meta-Llama-3-8B_fp8_quark-Q3_K_L.gguf) | 4121.74 MB |
| Q3_K_M | [amd-Meta-Llama-3-8B_fp8_quark-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/amd-Meta-Llama-3-8B_fp8_quark-GGUF/blob/main/amd-Meta-Llama-3-8B_fp8_quark-Q3_K_M.gguf) | 3832.74 MB |
| Q3_K_S | [amd-Meta-Llama-3-8B_fp8_quark-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/amd-Meta-Llama-3-8B_fp8_quark-GGUF/blob/main/amd-Meta-Llama-3-8B_fp8_quark-Q3_K_S.gguf) | 3494.74 MB |
| Q4_K_M | [amd-Meta-Llama-3-8B_fp8_quark-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/amd-Meta-Llama-3-8B_fp8_quark-GGUF/blob/main/amd-Meta-Llama-3-8B_fp8_quark-Q4_K_M.gguf) | 4692.78 MB |
| Q4_K_S | [amd-Meta-Llama-3-8B_fp8_quark-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/amd-Meta-Llama-3-8B_fp8_quark-GGUF/blob/main/amd-Meta-Llama-3-8B_fp8_quark-Q4_K_S.gguf) | 4475.28 MB |
| Q5_K_M | [amd-Meta-Llama-3-8B_fp8_quark-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/amd-Meta-Llama-3-8B_fp8_quark-GGUF/blob/main/amd-Meta-Llama-3-8B_fp8_quark-Q5_K_M.gguf) | 5467.40 MB |
| Q5_K_S | [amd-Meta-Llama-3-8B_fp8_quark-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/amd-Meta-Llama-3-8B_fp8_quark-GGUF/blob/main/amd-Meta-Llama-3-8B_fp8_quark-Q5_K_S.gguf) | 5339.90 MB |
| Q6_K | [amd-Meta-Llama-3-8B_fp8_quark-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/amd-Meta-Llama-3-8B_fp8_quark-GGUF/blob/main/amd-Meta-Llama-3-8B_fp8_quark-Q6_K.gguf) | 6290.44 MB |
| Q8_0 | [amd-Meta-Llama-3-8B_fp8_quark-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/amd-Meta-Llama-3-8B_fp8_quark-GGUF/blob/main/amd-Meta-Llama-3-8B_fp8_quark-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/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/XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-GGUF | featherless-ai-quants | 2024-11-10T19:37:54Z | 21 | 0 | null | [
"gguf",
"text-generation",
"base_model:XavierSpycy/Meta-Llama-3-8B-Instruct-zh-10k",
"base_model:quantized:XavierSpycy/Meta-Llama-3-8B-Instruct-zh-10k",
"endpoints_compatible",
"region:us",
"conversational"
]
| text-generation | 2024-11-05T03:22:45Z | ---
base_model: XavierSpycy/Meta-Llama-3-8B-Instruct-zh-10k
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# XavierSpycy/Meta-Llama-3-8B-Instruct-zh-10k 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 | [XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-GGUF/blob/main/XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-IQ4_XS.gguf) | 4276.62 MB |
| Q2_K | [XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-GGUF/blob/main/XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-Q2_K.gguf) | 3031.86 MB |
| Q3_K_L | [XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-GGUF/blob/main/XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-Q3_K_L.gguf) | 4121.74 MB |
| Q3_K_M | [XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-GGUF/blob/main/XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-Q3_K_M.gguf) | 3832.74 MB |
| Q3_K_S | [XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-GGUF/blob/main/XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-Q3_K_S.gguf) | 3494.74 MB |
| Q4_K_M | [XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-GGUF/blob/main/XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-Q4_K_M.gguf) | 4692.78 MB |
| Q4_K_S | [XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-GGUF/blob/main/XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-Q4_K_S.gguf) | 4475.28 MB |
| Q5_K_M | [XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-GGUF/blob/main/XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-Q5_K_M.gguf) | 5467.40 MB |
| Q5_K_S | [XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-GGUF/blob/main/XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-Q5_K_S.gguf) | 5339.90 MB |
| Q6_K | [XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-GGUF/blob/main/XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-Q6_K.gguf) | 6290.44 MB |
| Q8_0 | [XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-GGUF/blob/main/XavierSpycy-Meta-Llama-3-8B-Instruct-zh-10k-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/OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-GGUF | featherless-ai-quants | 2024-11-10T19:37:33Z | 14 | 0 | null | [
"gguf",
"text-generation",
"base_model:OpenRLHF/Llama-2-13b-sft-model-ocra-500k",
"base_model:quantized:OpenRLHF/Llama-2-13b-sft-model-ocra-500k",
"endpoints_compatible",
"region:us",
"conversational"
]
| text-generation | 2024-11-05T02:08:04Z | ---
base_model: OpenLLMAI/Llama-2-13b-sft-model-ocra-500k
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# OpenLLMAI/Llama-2-13b-sft-model-ocra-500k 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 | [OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-GGUF/blob/main/OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-IQ4_XS.gguf) | 6694.34 MB |
| Q2_K | [OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-GGUF/blob/main/OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-Q2_K.gguf) | 4629.39 MB |
| Q3_K_L | [OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-GGUF/blob/main/OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-Q3_K_L.gguf) | 6608.54 MB |
| Q3_K_M | [OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-GGUF/blob/main/OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-Q3_K_M.gguf) | 6044.17 MB |
| Q3_K_S | [OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-GGUF/blob/main/OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-Q3_K_S.gguf) | 5396.83 MB |
| Q4_K_M | [OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-GGUF/blob/main/OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-Q4_K_M.gguf) | 7501.56 MB |
| Q4_K_S | [OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-GGUF/blob/main/OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-Q4_K_S.gguf) | 7079.30 MB |
| Q5_K_M | [OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-GGUF/blob/main/OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-Q5_K_M.gguf) | 8802.34 MB |
| Q5_K_S | [OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-GGUF/blob/main/OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-Q5_K_S.gguf) | 8556.64 MB |
| Q6_K | [OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-GGUF/blob/main/OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-Q6_K.gguf) | 10184.42 MB |
| Q8_0 | [OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-GGUF/blob/main/OpenLLMAI-Llama-2-13b-sft-model-ocra-500k-Q8_0.gguf) | 13190.58 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/devhyun88-hyun-mistral-7b-orca-platypus-refine-GGUF | featherless-ai-quants | 2024-11-10T19:37:29Z | 8 | 0 | null | [
"gguf",
"text-generation",
"base_model:devhyun88/hyun-mistral-7b-orca-platypus-refine",
"base_model:quantized:devhyun88/hyun-mistral-7b-orca-platypus-refine",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-11-05T02:00:09Z | ---
base_model: devhyun88/hyun-mistral-7b-orca-platypus-refine
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# devhyun88/hyun-mistral-7b-orca-platypus-refine 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 | [devhyun88-hyun-mistral-7b-orca-platypus-refine-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/devhyun88-hyun-mistral-7b-orca-platypus-refine-GGUF/blob/main/devhyun88-hyun-mistral-7b-orca-platypus-refine-IQ4_XS.gguf) | 3761.66 MB |
| Q2_K | [devhyun88-hyun-mistral-7b-orca-platypus-refine-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/devhyun88-hyun-mistral-7b-orca-platypus-refine-GGUF/blob/main/devhyun88-hyun-mistral-7b-orca-platypus-refine-Q2_K.gguf) | 2593.27 MB |
| Q3_K_L | [devhyun88-hyun-mistral-7b-orca-platypus-refine-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/devhyun88-hyun-mistral-7b-orca-platypus-refine-GGUF/blob/main/devhyun88-hyun-mistral-7b-orca-platypus-refine-Q3_K_L.gguf) | 3644.97 MB |
| Q3_K_M | [devhyun88-hyun-mistral-7b-orca-platypus-refine-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/devhyun88-hyun-mistral-7b-orca-platypus-refine-GGUF/blob/main/devhyun88-hyun-mistral-7b-orca-platypus-refine-Q3_K_M.gguf) | 3355.97 MB |
| Q3_K_S | [devhyun88-hyun-mistral-7b-orca-platypus-refine-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/devhyun88-hyun-mistral-7b-orca-platypus-refine-GGUF/blob/main/devhyun88-hyun-mistral-7b-orca-platypus-refine-Q3_K_S.gguf) | 3017.97 MB |
| Q4_K_M | [devhyun88-hyun-mistral-7b-orca-platypus-refine-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/devhyun88-hyun-mistral-7b-orca-platypus-refine-GGUF/blob/main/devhyun88-hyun-mistral-7b-orca-platypus-refine-Q4_K_M.gguf) | 4166.07 MB |
| Q4_K_S | [devhyun88-hyun-mistral-7b-orca-platypus-refine-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/devhyun88-hyun-mistral-7b-orca-platypus-refine-GGUF/blob/main/devhyun88-hyun-mistral-7b-orca-platypus-refine-Q4_K_S.gguf) | 3948.57 MB |
| Q5_K_M | [devhyun88-hyun-mistral-7b-orca-platypus-refine-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/devhyun88-hyun-mistral-7b-orca-platypus-refine-GGUF/blob/main/devhyun88-hyun-mistral-7b-orca-platypus-refine-Q5_K_M.gguf) | 4893.69 MB |
| Q5_K_S | [devhyun88-hyun-mistral-7b-orca-platypus-refine-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/devhyun88-hyun-mistral-7b-orca-platypus-refine-GGUF/blob/main/devhyun88-hyun-mistral-7b-orca-platypus-refine-Q5_K_S.gguf) | 4766.19 MB |
| Q6_K | [devhyun88-hyun-mistral-7b-orca-platypus-refine-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/devhyun88-hyun-mistral-7b-orca-platypus-refine-GGUF/blob/main/devhyun88-hyun-mistral-7b-orca-platypus-refine-Q6_K.gguf) | 5666.80 MB |
| Q8_0 | [devhyun88-hyun-mistral-7b-orca-platypus-refine-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/devhyun88-hyun-mistral-7b-orca-platypus-refine-GGUF/blob/main/devhyun88-hyun-mistral-7b-orca-platypus-refine-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/awnr-Mistral-7B-v0.1-signtensors-1-over-4-GGUF | featherless-ai-quants | 2024-11-10T19:37:21Z | 8 | 0 | null | [
"gguf",
"text-generation",
"base_model:awnr/Mistral-7B-v0.1-signtensors-1-over-4",
"base_model:quantized:awnr/Mistral-7B-v0.1-signtensors-1-over-4",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-11-05T01:23:59Z | ---
base_model: awnr/Mistral-7B-v0.1-signtensors-1-over-4
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# awnr/Mistral-7B-v0.1-signtensors-1-over-4 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 | [awnr-Mistral-7B-v0.1-signtensors-1-over-4-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/awnr-Mistral-7B-v0.1-signtensors-1-over-4-GGUF/blob/main/awnr-Mistral-7B-v0.1-signtensors-1-over-4-IQ4_XS.gguf) | 3761.66 MB |
| Q2_K | [awnr-Mistral-7B-v0.1-signtensors-1-over-4-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/awnr-Mistral-7B-v0.1-signtensors-1-over-4-GGUF/blob/main/awnr-Mistral-7B-v0.1-signtensors-1-over-4-Q2_K.gguf) | 2593.27 MB |
| Q3_K_L | [awnr-Mistral-7B-v0.1-signtensors-1-over-4-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/awnr-Mistral-7B-v0.1-signtensors-1-over-4-GGUF/blob/main/awnr-Mistral-7B-v0.1-signtensors-1-over-4-Q3_K_L.gguf) | 3644.97 MB |
| Q3_K_M | [awnr-Mistral-7B-v0.1-signtensors-1-over-4-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/awnr-Mistral-7B-v0.1-signtensors-1-over-4-GGUF/blob/main/awnr-Mistral-7B-v0.1-signtensors-1-over-4-Q3_K_M.gguf) | 3355.97 MB |
| Q3_K_S | [awnr-Mistral-7B-v0.1-signtensors-1-over-4-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/awnr-Mistral-7B-v0.1-signtensors-1-over-4-GGUF/blob/main/awnr-Mistral-7B-v0.1-signtensors-1-over-4-Q3_K_S.gguf) | 3017.97 MB |
| Q4_K_M | [awnr-Mistral-7B-v0.1-signtensors-1-over-4-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/awnr-Mistral-7B-v0.1-signtensors-1-over-4-GGUF/blob/main/awnr-Mistral-7B-v0.1-signtensors-1-over-4-Q4_K_M.gguf) | 4166.07 MB |
| Q4_K_S | [awnr-Mistral-7B-v0.1-signtensors-1-over-4-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/awnr-Mistral-7B-v0.1-signtensors-1-over-4-GGUF/blob/main/awnr-Mistral-7B-v0.1-signtensors-1-over-4-Q4_K_S.gguf) | 3948.57 MB |
| Q5_K_M | [awnr-Mistral-7B-v0.1-signtensors-1-over-4-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/awnr-Mistral-7B-v0.1-signtensors-1-over-4-GGUF/blob/main/awnr-Mistral-7B-v0.1-signtensors-1-over-4-Q5_K_M.gguf) | 4893.69 MB |
| Q5_K_S | [awnr-Mistral-7B-v0.1-signtensors-1-over-4-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/awnr-Mistral-7B-v0.1-signtensors-1-over-4-GGUF/blob/main/awnr-Mistral-7B-v0.1-signtensors-1-over-4-Q5_K_S.gguf) | 4766.19 MB |
| Q6_K | [awnr-Mistral-7B-v0.1-signtensors-1-over-4-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/awnr-Mistral-7B-v0.1-signtensors-1-over-4-GGUF/blob/main/awnr-Mistral-7B-v0.1-signtensors-1-over-4-Q6_K.gguf) | 5666.79 MB |
| Q8_0 | [awnr-Mistral-7B-v0.1-signtensors-1-over-4-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/awnr-Mistral-7B-v0.1-signtensors-1-over-4-GGUF/blob/main/awnr-Mistral-7B-v0.1-signtensors-1-over-4-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/AuriAetherwiing-MN-12B-Starcannon-v2-GGUF | featherless-ai-quants | 2024-11-10T19:37:08Z | 76 | 0 | null | [
"gguf",
"text-generation",
"base_model:AuriAetherwiing/MN-12B-Starcannon-v2",
"base_model:quantized:AuriAetherwiing/MN-12B-Starcannon-v2",
"endpoints_compatible",
"region:us",
"conversational"
]
| text-generation | 2024-11-05T01:03:51Z | ---
base_model: AuriAetherwiing/MN-12B-Starcannon-v2
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# AuriAetherwiing/MN-12B-Starcannon-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 | [AuriAetherwiing-MN-12B-Starcannon-v2-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/AuriAetherwiing-MN-12B-Starcannon-v2-GGUF/blob/main/AuriAetherwiing-MN-12B-Starcannon-v2-IQ4_XS.gguf) | 6485.04 MB |
| Q2_K | [AuriAetherwiing-MN-12B-Starcannon-v2-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/AuriAetherwiing-MN-12B-Starcannon-v2-GGUF/blob/main/AuriAetherwiing-MN-12B-Starcannon-v2-Q2_K.gguf) | 4569.10 MB |
| Q3_K_L | [AuriAetherwiing-MN-12B-Starcannon-v2-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/AuriAetherwiing-MN-12B-Starcannon-v2-GGUF/blob/main/AuriAetherwiing-MN-12B-Starcannon-v2-Q3_K_L.gguf) | 6257.54 MB |
| Q3_K_M | [AuriAetherwiing-MN-12B-Starcannon-v2-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/AuriAetherwiing-MN-12B-Starcannon-v2-GGUF/blob/main/AuriAetherwiing-MN-12B-Starcannon-v2-Q3_K_M.gguf) | 5801.29 MB |
| Q3_K_S | [AuriAetherwiing-MN-12B-Starcannon-v2-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/AuriAetherwiing-MN-12B-Starcannon-v2-GGUF/blob/main/AuriAetherwiing-MN-12B-Starcannon-v2-Q3_K_S.gguf) | 5277.85 MB |
| Q4_K_M | [AuriAetherwiing-MN-12B-Starcannon-v2-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/AuriAetherwiing-MN-12B-Starcannon-v2-GGUF/blob/main/AuriAetherwiing-MN-12B-Starcannon-v2-Q4_K_M.gguf) | 7130.82 MB |
| Q4_K_S | [AuriAetherwiing-MN-12B-Starcannon-v2-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/AuriAetherwiing-MN-12B-Starcannon-v2-GGUF/blob/main/AuriAetherwiing-MN-12B-Starcannon-v2-Q4_K_S.gguf) | 6790.35 MB |
| Q5_K_M | [AuriAetherwiing-MN-12B-Starcannon-v2-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/AuriAetherwiing-MN-12B-Starcannon-v2-GGUF/blob/main/AuriAetherwiing-MN-12B-Starcannon-v2-Q5_K_M.gguf) | 8323.32 MB |
| Q5_K_S | [AuriAetherwiing-MN-12B-Starcannon-v2-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/AuriAetherwiing-MN-12B-Starcannon-v2-GGUF/blob/main/AuriAetherwiing-MN-12B-Starcannon-v2-Q5_K_S.gguf) | 8124.10 MB |
| Q6_K | [AuriAetherwiing-MN-12B-Starcannon-v2-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/AuriAetherwiing-MN-12B-Starcannon-v2-GGUF/blob/main/AuriAetherwiing-MN-12B-Starcannon-v2-Q6_K.gguf) | 9590.35 MB |
| Q8_0 | [AuriAetherwiing-MN-12B-Starcannon-v2-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/AuriAetherwiing-MN-12B-Starcannon-v2-GGUF/blob/main/AuriAetherwiing-MN-12B-Starcannon-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/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/wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-GGUF | featherless-ai-quants | 2024-11-10T19:36:55Z | 6 | 0 | null | [
"gguf",
"text-generation",
"base_model:wang7776/Mistral-7B-Instruct-v0.2-sparsity-10",
"base_model:quantized:wang7776/Mistral-7B-Instruct-v0.2-sparsity-10",
"endpoints_compatible",
"region:us",
"conversational"
]
| text-generation | 2024-11-05T00:03:19Z | ---
base_model: wang7776/Mistral-7B-Instruct-v0.2-sparsity-10
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# wang7776/Mistral-7B-Instruct-v0.2-sparsity-10 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 | [wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-GGUF/blob/main/wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-IQ4_XS.gguf) | 3761.66 MB |
| Q2_K | [wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-GGUF/blob/main/wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-Q2_K.gguf) | 2593.27 MB |
| Q3_K_L | [wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-GGUF/blob/main/wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-Q3_K_L.gguf) | 3644.97 MB |
| Q3_K_M | [wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-GGUF/blob/main/wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-Q3_K_M.gguf) | 3355.97 MB |
| Q3_K_S | [wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-GGUF/blob/main/wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-Q3_K_S.gguf) | 3017.97 MB |
| Q4_K_M | [wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-GGUF/blob/main/wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-Q4_K_M.gguf) | 4166.07 MB |
| Q4_K_S | [wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-GGUF/blob/main/wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-Q4_K_S.gguf) | 3948.57 MB |
| Q5_K_M | [wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-GGUF/blob/main/wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-Q5_K_M.gguf) | 4893.69 MB |
| Q5_K_S | [wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-GGUF/blob/main/wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-Q5_K_S.gguf) | 4766.19 MB |
| Q6_K | [wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-GGUF/blob/main/wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-Q6_K.gguf) | 5666.80 MB |
| Q8_0 | [wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-GGUF/blob/main/wang7776-Mistral-7B-Instruct-v0.2-sparsity-10-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/fblgit-una-cybertron-7b-v1-fp16-GGUF | featherless-ai-quants | 2024-11-10T19:36:54Z | 11 | 0 | null | [
"gguf",
"text-generation",
"base_model:fblgit/una-cybertron-7b-v1-fp16",
"base_model:quantized:fblgit/una-cybertron-7b-v1-fp16",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-11-04T23:56:42Z | ---
base_model: fblgit/una-cybertron-7b-v1-fp16
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# fblgit/una-cybertron-7b-v1-fp16 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 | [fblgit-una-cybertron-7b-v1-fp16-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/fblgit-una-cybertron-7b-v1-fp16-GGUF/blob/main/fblgit-una-cybertron-7b-v1-fp16-IQ4_XS.gguf) | 3761.66 MB |
| Q2_K | [fblgit-una-cybertron-7b-v1-fp16-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/fblgit-una-cybertron-7b-v1-fp16-GGUF/blob/main/fblgit-una-cybertron-7b-v1-fp16-Q2_K.gguf) | 2593.27 MB |
| Q3_K_L | [fblgit-una-cybertron-7b-v1-fp16-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/fblgit-una-cybertron-7b-v1-fp16-GGUF/blob/main/fblgit-una-cybertron-7b-v1-fp16-Q3_K_L.gguf) | 3644.97 MB |
| Q3_K_M | [fblgit-una-cybertron-7b-v1-fp16-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/fblgit-una-cybertron-7b-v1-fp16-GGUF/blob/main/fblgit-una-cybertron-7b-v1-fp16-Q3_K_M.gguf) | 3355.97 MB |
| Q3_K_S | [fblgit-una-cybertron-7b-v1-fp16-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/fblgit-una-cybertron-7b-v1-fp16-GGUF/blob/main/fblgit-una-cybertron-7b-v1-fp16-Q3_K_S.gguf) | 3017.97 MB |
| Q4_K_M | [fblgit-una-cybertron-7b-v1-fp16-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/fblgit-una-cybertron-7b-v1-fp16-GGUF/blob/main/fblgit-una-cybertron-7b-v1-fp16-Q4_K_M.gguf) | 4166.07 MB |
| Q4_K_S | [fblgit-una-cybertron-7b-v1-fp16-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/fblgit-una-cybertron-7b-v1-fp16-GGUF/blob/main/fblgit-una-cybertron-7b-v1-fp16-Q4_K_S.gguf) | 3948.57 MB |
| Q5_K_M | [fblgit-una-cybertron-7b-v1-fp16-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/fblgit-una-cybertron-7b-v1-fp16-GGUF/blob/main/fblgit-una-cybertron-7b-v1-fp16-Q5_K_M.gguf) | 4893.69 MB |
| Q5_K_S | [fblgit-una-cybertron-7b-v1-fp16-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/fblgit-una-cybertron-7b-v1-fp16-GGUF/blob/main/fblgit-una-cybertron-7b-v1-fp16-Q5_K_S.gguf) | 4766.19 MB |
| Q6_K | [fblgit-una-cybertron-7b-v1-fp16-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/fblgit-una-cybertron-7b-v1-fp16-GGUF/blob/main/fblgit-una-cybertron-7b-v1-fp16-Q6_K.gguf) | 5666.80 MB |
| Q8_0 | [fblgit-una-cybertron-7b-v1-fp16-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/fblgit-una-cybertron-7b-v1-fp16-GGUF/blob/main/fblgit-una-cybertron-7b-v1-fp16-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) |
iamjoshgreen/mspackage | iamjoshgreen | 2024-11-10T19:32:43Z | 6 | 0 | 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 | 2024-11-10T19:03:19Z | ---
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: mspackage
---
# Mspackage
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `mspackage` 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('iamjoshgreen/mspackage', 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)
|
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).
|
waloneai/mawc | waloneai | 2024-11-10T19:19:01Z | 6 | 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 | 2024-11-10T19:18:57Z | ---
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
<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/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
```
|
ihughes15234/phi35_tictactoe_dpo2epoch_v5 | ihughes15234 | 2024-11-10T19:14:02Z | 79 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:ihughes15234/phi35_tictactoe_dpo1epoch_v5",
"base_model:finetune:ihughes15234/phi35_tictactoe_dpo1epoch_v5",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-11-10T18:58:50Z | ---
base_model: ihughes15234/phi35_tictactoe_dpo1epoch_v5
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_dpo1epoch_v5
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/DataVortexS-10.7B-dpo-v1.7-i1-GGUF | mradermacher | 2024-11-10T19:07:12Z | 18 | 0 | transformers | [
"transformers",
"gguf",
"text-generation",
"ko",
"base_model:Edentns/DataVortexS-10.7B-dpo-v1.7",
"base_model:quantized:Edentns/DataVortexS-10.7B-dpo-v1.7",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
]
| text-generation | 2024-11-10T09:29:12Z | ---
base_model: Edentns/DataVortexS-10.7B-dpo-v1.7
language:
- ko
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
tags:
- text-generation
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Edentns/DataVortexS-10.7B-dpo-v1.7
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.7-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/DataVortexS-10.7B-dpo-v1.7-i1-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.7.i1-IQ1_S.gguf) | i1-IQ1_S | 2.5 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.7-i1-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.7.i1-IQ1_M.gguf) | i1-IQ1_M | 2.7 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.7-i1-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.7.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.7-i1-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.7.i1-IQ2_XS.gguf) | i1-IQ2_XS | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.7-i1-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.7.i1-IQ2_S.gguf) | i1-IQ2_S | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.7-i1-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.7.i1-IQ2_M.gguf) | i1-IQ2_M | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.7-i1-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.7.i1-Q2_K.gguf) | i1-Q2_K | 4.2 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.7-i1-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.7.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 4.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.7-i1-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.7.i1-IQ3_XS.gguf) | i1-IQ3_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.7-i1-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.7.i1-Q3_K_S.gguf) | i1-Q3_K_S | 4.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.7-i1-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.7.i1-IQ3_S.gguf) | i1-IQ3_S | 4.9 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.7-i1-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.7.i1-IQ3_M.gguf) | i1-IQ3_M | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.7-i1-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.7.i1-Q3_K_M.gguf) | i1-Q3_K_M | 5.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.7-i1-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.7.i1-Q3_K_L.gguf) | i1-Q3_K_L | 5.8 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.7-i1-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.7.i1-IQ4_XS.gguf) | i1-IQ4_XS | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.7-i1-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.7.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 6.3 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.7-i1-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.7.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 6.3 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.7-i1-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.7.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 6.3 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.7-i1-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.7.i1-Q4_0.gguf) | i1-Q4_0 | 6.3 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.7-i1-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.7.i1-Q4_K_S.gguf) | i1-Q4_K_S | 6.3 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.7-i1-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.7.i1-Q4_K_M.gguf) | i1-Q4_K_M | 6.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.7-i1-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.7.i1-Q5_K_S.gguf) | i1-Q5_K_S | 7.6 | |
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.7-i1-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.7.i1-Q5_K_M.gguf) | i1-Q5_K_M | 7.8 | |
| [GGUF](https://huggingface.co/mradermacher/DataVortexS-10.7B-dpo-v1.7-i1-GGUF/resolve/main/DataVortexS-10.7B-dpo-v1.7.i1-Q6_K.gguf) | i1-Q6_K | 9.0 | 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 -->
|
huyquoctrinh/musicgen-melody-lora-punk | huyquoctrinh | 2024-11-10T19:06:08Z | 5 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"musicgen_melody",
"text-to-audio",
"ylacombe/tiny-punk",
"generated_from_trainer",
"base_model:facebook/musicgen-melody",
"base_model:adapter:facebook/musicgen-melody",
"license:cc-by-nc-4.0",
"region:us"
]
| text-to-audio | 2024-11-10T18:58:59Z | ---
library_name: peft
license: cc-by-nc-4.0
base_model: facebook/musicgen-melody
tags:
- text-to-audio
- ylacombe/tiny-punk
- generated_from_trainer
model-index:
- name: musicgen-melody-lora-punk
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. -->
# musicgen-melody-lora-punk
This model is a fine-tuned version of [facebook/musicgen-melody](https://huggingface.co/facebook/musicgen-melody) on the YLACOMBE/TINY-PUNK - DEFAULT dataset.
It achieves the following results on the evaluation set:
- Loss: 4.7421
- Clap: -0.0067
## 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: 0.0002
- train_batch_size: 4
- eval_batch_size: 2
- seed: 456
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.99) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3 |
Ttimofeyka/Tissint-14B-128k-RP | Ttimofeyka | 2024-11-10T19:04:21Z | 16 | 5 | transformers | [
"transformers",
"pytorch",
"qwen2",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"base_model:arcee-ai/SuperNova-Medius",
"base_model:finetune:arcee-ai/SuperNova-Medius",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-11-09T16:36:09Z | ---
base_model:
- arcee-ai/SuperNova-Medius
library_name: transformers
license: apache-2.0
tags:
- unsloth
- trl
- sft
---
# Tissint-14B-128k-RP
---
---
The model is based on [SuperNova-Medius](https://huggingface.co/arcee-ai/SuperNova-Medius) (as the current best 14B model) with a 128k context with an emphasis on creativity, including NSFW and multi-turn conversations.
According to my tests, this finetune is much more stable with different samplers than the original model. Censorship and refusals have been reduced.
The model started to follow the system prompt better, and the responses in ChatML format with bad samplers stopped reaching 800+ tokens for no reason.
### Chat Template - ChatML
## Samplers
### Balance
```
Temp : 0.8 - 1.15
Min P : 0.1
Repetition Penalty : 1.02
DRY 0.8, 1.75, 2, 2048 (change to 4096 or more if needed)
```
### Creativity
```
Temp : 1.15 - 1.5
Top P : 0.9
Repetition Penalty : 1.03
DRY 0.82, 1.75, 2, 2048 (change to 4096 or more if needed)
``` |
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] |
AbuZaforCSE/BanglaFinGPT | AbuZaforCSE | 2024-11-10T18:56:29Z | 76 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
]
| text-generation | 2024-11-10T18:42:02Z | ---
library_name: transformers
tags:
- trl
- sft
---
# 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] |
alidenewade/unit_5_exercise | alidenewade | 2024-11-10T18:54:34Z | 88 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dv",
"dataset:mozilla-foundation/common_voice_13_0",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2024-11-10T18:11:41Z | ---
library_name: transformers
language:
- dv
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Unit 5 Ali's exercise
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 13 (Alid)
type: mozilla-foundation/common_voice_13_0
config: dv
split: test
args: dv
metrics:
- name: Wer
type: wer
value: 116.39426922140697
---
<!-- 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. -->
# Unit 5 Ali's exercise
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 13 (Alid) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9533
- Wer Ortho: 223.8248
- Wer: 116.3943
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 550
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:------:|:----:|:---------------:|:---------:|:--------:|
| 0.9416 | 1.6287 | 500 | 0.9533 | 223.8248 | 116.3943 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1
|
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)
|
SufficientPrune3897/magnum-v4-123b-exl2-RPCAL-2.6bpw | SufficientPrune3897 | 2024-11-10T18:27:53Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"chat",
"conversational",
"en",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
]
| text-generation | 2024-11-10T16:22:34Z | ---
license: other
license_name: mrl
language:
- en
tags:
- chat
pipeline_tag: text-generation
library_name: transformers
---
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
... |
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 -->
|
pxyyy/rlhflow_mixture_clean_empty_round_with_dart_scalebiosampled-600k | pxyyy | 2024-11-10T18:24:17Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-11-10T18:16:41Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
```
#!/bin/bash
#SBATCH --job-name="fintune"
#SBATCH --partition=ghx4
#SBATCH --nodes=1
#SBATCH --gpus-per-node=4
#SBATCH --tasks=1
#SBATCH --tasks-per-node=1
#SBATCH --cpus-per-task=20
#SBATCH --mem=512g
#SBATCH --time=23:59:00
#SBATCH --output="run.log"
#SBATCH --error="run.err"
set -e
export WANDB_API_KEY='1b2611814911cad498235f1ccb1a2e182638bd62'
# set up exp1 or exp3!!!!!
# launch this script after bilevel weighting and preparing data
# this script is for exp1 and exp3
# 1. finetune on bilevel and baseline
CUDA_VISIBLE=0,1,2,3
hf_ds=pxyyy/rlhflow_mixture_clean_empty_round_with_dart-math_scalebiosampled-600k
hf_val_ds=pxyyy/rlhflow_scalbio_test
model_and_tok=meta-llama/Meta-Llama-3-8B
conv_template=llama3
hf_ds_str=$(echo ${hf_ds}|sed 's/\//-/g')
tmp_data_dir=./tmp_data/${hf_ds_str}/
val_data_dir=./tmp_data/${hf_ds_str}_val/
mkdir -p ${tmp_data_dir}
mkdir -p ${val_data_dir}
python3 hf2lmflow.py --ds_name ${hf_ds} --save ${tmp_data_dir}/data.json
python3 hf2lmflow.py --ds_name ${hf_val_ds} --save ${val_data_dir}/data.json
model_str=$(echo ${model_and_tok}|sed 's/\//-/g')
lisa_activated_layers=2
lisa_interval_steps=20
gradient_accumulation_steps=2
per_device_train_batch_size=8
epoch=1
project_dir=/u/xpan2/projects/scalebio/finetune/
for lr in 2e-5
do
# Finetune
exp_id=scalebio-scalebio-${model_str}-${hf_ds_str}-${epoch}-$lr-lisa_${lisa_activated_layers}_${lisa_interval_steps}
# project_dir=$(cd "$(dirname $0)"; pwd)
log_dir=${project_dir}/log/${exp_id}
output_dir=${project_dir}/output_models/${exp_id}
echo $exp_id
mkdir -p ${output_dir} ${log_dir}
export TRANSFORMERS_VERBOSITY=info
deepspeed --master_port=7964 --include=localhost:${CUDA_VISIBLE} finetune.py \
--model_name_or_path ${model_and_tok} \
--trust_remote_code 1 \
--dataset_path ${tmp_data_dir}/ \
--eval_dataset_path ${val_data_dir}/ \
--output_dir ${output_dir} --overwrite_output_dir \
--conversation_template ${conv_template} \
--num_train_epochs $epoch \
--learning_rate $lr \
--disable_group_texts 1 \
--block_size 1024 \
--per_device_train_batch_size ${per_device_train_batch_size} \
--per_device_eval_batch_size 1 \
--bf16 \
--deepspeed configs/ds_config_zero2_no_offload.json \
--torch_dtype bfloat16 \
--run_name ${exp_id} \
--optim adamw_torch_fused \
--logging_steps 1 \
--do_train \
--do_eval \
--ddp_timeout 72000 \
--save_total_limit 1 \
--load_best_model_at_end False \
--eval_steps 10 \
--save_only_model \
--evaluation_strategy "steps" \
--dataloader_num_workers 1 \
--lr_scheduler_type cosine \
--warmup_ratio 0.03 \
--gradient_checkpointing True \
--use_flash_attention 1 \
--gradient_accumulation_steps ${gradient_accumulation_steps} \
--lisa_activated_layers ${lisa_activated_layers} \
--lisa_interval_steps ${lisa_interval_steps} \
| tee ${log_dir}/train.log \
2> ${log_dir}/train.err
done
```
`no lisa`
## 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] |
borisf/best-ludka1-bob | borisf | 2024-11-10T18:22:40Z | 29 | 0 | diffusers | [
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"fluxgym",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
]
| text-to-image | 2024-11-10T17:25:08Z | ---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: ludka1
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
---
# best-ludka1-bob
A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
<Gallery />
## Trigger words
You should use `ludka1` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.
|
cuongdev/3nguoi-2000 | cuongdev | 2024-11-10T18:21:05Z | 29 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2024-11-10T18:15:34Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### 3nguoi-2000 Dreambooth model trained by cuongdev with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
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
|
harshvardhanj733/results_spanish | harshvardhanj733 | 2024-11-10T18:05:40Z | 179 | 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 | 2024-11-10T18:04:43Z | ---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: results_spanish
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_spanish
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: 0.6650
- Accuracy: 0.8723
- Precision: 0.8712
- Recall: 0.8723
- F1: 0.8716
## 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: 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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 1.0 | 258 | 0.8212 | 0.6518 | 0.5813 | 0.6518 | 0.6054 |
| 0.8699 | 2.0 | 516 | 0.6173 | 0.7795 | 0.7582 | 0.7795 | 0.7637 |
| 0.8699 | 3.0 | 774 | 0.6379 | 0.8182 | 0.8323 | 0.8182 | 0.8221 |
| 0.4077 | 4.0 | 1032 | 0.5788 | 0.8607 | 0.8625 | 0.8607 | 0.8612 |
| 0.4077 | 5.0 | 1290 | 0.6009 | 0.8530 | 0.8534 | 0.8530 | 0.8532 |
| 0.2701 | 6.0 | 1548 | 0.6650 | 0.8723 | 0.8712 | 0.8723 | 0.8716 |
| 0.2701 | 7.0 | 1806 | 0.7184 | 0.8685 | 0.8694 | 0.8685 | 0.8689 |
| 0.1895 | 8.0 | 2064 | 0.7405 | 0.8646 | 0.8658 | 0.8646 | 0.8651 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1
|
Lreneee/bert-finetuned-ner | Lreneee | 2024-11-10T18:05:18Z | 106 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"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 | 2024-11-10T17:09:48Z | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
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. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2326
- Precision: 0.4345
- Recall: 0.6512
- F1: 0.5212
- Accuracy: 0.9357
## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use 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: 100
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 63 | 0.4595 | 0.2973 | 0.0226 | 0.0421 | 0.9067 |
| No log | 2.0 | 126 | 0.2294 | 0.4714 | 0.5936 | 0.5255 | 0.9403 |
| No log | 3.0 | 189 | 0.2326 | 0.4345 | 0.6512 | 0.5212 | 0.9357 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.2.2
- Datasets 3.1.0
- Tokenizers 0.20.3
|
shafitanvir31/bert-base-multilingual-cased-BanFakeFineTuned | shafitanvir31 | 2024-11-10T18:00:46Z | 106 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-11-10T18:00:24Z | ---
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] |
bryanchrist/MATHWELL | bryanchrist | 2024-11-10T17:57:01Z | 0 | 2 | peft | [
"peft",
"arxiv:2402.15861",
"license:gpl-3.0",
"region:us"
]
| null | 2024-02-21T23:33:15Z | ---
library_name: peft
license: gpl-3.0
---
## MATHWELL
MATHWELL is the model released in the paper [MATHWELL: Generating Educational Math Word Problems Using Teacher Annotations](https://arxiv.org/abs/2402.15861).
MATHWELL is a finetuned Llama-2 (70B) model that generates customized educational grade school math word problems and Python function solutions to these problems. Generated problems are 1) solvable, 2) accurate, and 3) appropriate. These criteria are essential to successfully supplement grade-school students’ math education. On average, 74% of MATHWELL's problems with executable solutions are solvable, accurate, and appropriate.
For more details on how MATHWELL was trained and evaluated, please see our [paper](https://arxiv.org/abs/2402.15861). Our [repo](https://github.com/bryanchrist/MATHWELL) contains a sample script for loading and interacting with MATHWELL.
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
## Citation
```bash
@inproceedings{christ-etal-2024-mathwell,
title = "{MATHWELL}: Generating Educational Math Word Problems Using Teacher Annotations",
author = "Christ, Bryan R and
Kropko, Jonathan and
Hartvigsen, Thomas",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.696",
pages = "11914--11938",
abstract = "Math word problems are critical K-8 educational tools, but writing them is time consuming and requires extensive expertise. To be educational, problems must be solvable, have accurate answers, and, most importantly, be educationally appropriate. We propose that language models have potential to support K-8 math education by automatically generating word problems. However, evaluating educational appropriateness is hard to quantify. We fill this gap by having teachers evaluate problems generated by LLMs, who find existing models and data often fail to be educationally appropriate. We then explore automatically generating *educational* word problems, ultimately using our expert annotations to finetune a 70B language model. Our model, MATHWELL, is the first K-8 word problem generator targeted at educational appropriateness. Further expert studies find MATHWELL generates problems far more solvable, accurate, and appropriate than public models. MATHWELL also matches GPT-4{'}s problem quality while attaining more appropriate reading levels for K-8 students and avoiding generating harmful questions.",
}
``` |
recoilme/recoilme-sdxl-v12 | recoilme | 2024-11-10T17:56:22Z | 46 | 0 | diffusers | [
"diffusers",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
]
| text-to-image | 2024-11-10T17:44:50Z | ---
license: cc-by-nc-4.0
---
|
Rubywong123/AgentGrow-shopping | Rubywong123 | 2024-11-10T17:55:23Z | 5 | 0 | null | [
"safetensors",
"llama",
"en",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct",
"license:mit",
"region:us"
]
| null | 2024-11-07T17:18:20Z | ---
license: mit
language:
- en
base_model:
- meta-llama/Meta-Llama-3-8B-Instruct
--- |
furrutiav/roberta_mixtral_nllfg_vanilla_sst2 | furrutiav | 2024-11-10T17:50:43Z | 103 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"feature-extraction",
"arxiv:1910.09700",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| feature-extraction | 2024-11-06T18:47:12Z | ---
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] |
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
|
featherless-ai-quants/KaraKaraWitch-SteyrCannon-Qwen2.5-72b-GGUF | featherless-ai-quants | 2024-11-10T17:49:25Z | 16 | 0 | null | [
"gguf",
"text-generation",
"base_model:KaraKaraWitch/SteyrCannon-Qwen2.5-72b",
"base_model:quantized:KaraKaraWitch/SteyrCannon-Qwen2.5-72b",
"endpoints_compatible",
"region:us",
"conversational"
]
| text-generation | 2024-11-09T21:43:05Z | ---
base_model: KaraKaraWitch/SteyrCannon-Qwen2.5-72b
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# KaraKaraWitch-SteyrCannon-Qwen2.5-72b 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 | [KaraKaraWitch-SteyrCannon-Qwen2.5-72b-IQ4_XS](https://huggingface.co/featherless-ai-quants/KaraKaraWitch-SteyrCannon-Qwen2.5-72b-GGUF/tree/main/KaraKaraWitch-SteyrCannon-Qwen2.5-72b-IQ4_XS) | 38302.65 MB (folder) |
| Q2_K | [KaraKaraWitch-SteyrCannon-Qwen2.5-72b-Q2_K](https://huggingface.co/featherless-ai-quants/KaraKaraWitch-SteyrCannon-Qwen2.5-72b-GGUF/tree/main/KaraKaraWitch-SteyrCannon-Qwen2.5-72b-Q2_K) | 28430.71 MB (folder) |
| Q3_K_L | [KaraKaraWitch-SteyrCannon-Qwen2.5-72b-Q3_K_L](https://huggingface.co/featherless-ai-quants/KaraKaraWitch-SteyrCannon-Qwen2.5-72b-GGUF/tree/main/KaraKaraWitch-SteyrCannon-Qwen2.5-72b-Q3_K_L) | 37675.12 MB (folder) |
| Q3_K_M | [KaraKaraWitch-SteyrCannon-Qwen2.5-72b-Q3_K_M](https://huggingface.co/featherless-ai-quants/KaraKaraWitch-SteyrCannon-Qwen2.5-72b-GGUF/tree/main/KaraKaraWitch-SteyrCannon-Qwen2.5-72b-Q3_K_M) | 35952.30 MB (folder) |
| Q3_K_S | [KaraKaraWitch-SteyrCannon-Qwen2.5-72b-Q3_K_S](https://huggingface.co/featherless-ai-quants/KaraKaraWitch-SteyrCannon-Qwen2.5-72b-GGUF/tree/main/KaraKaraWitch-SteyrCannon-Qwen2.5-72b-Q3_K_S) | 32890.12 MB (folder) |
| Q4_K_M | [KaraKaraWitch-SteyrCannon-Qwen2.5-72b-Q4_K_M](https://huggingface.co/featherless-ai-quants/KaraKaraWitch-SteyrCannon-Qwen2.5-72b-GGUF/tree/main/KaraKaraWitch-SteyrCannon-Qwen2.5-72b-Q4_K_M) | 45219.15 MB (folder) |
| Q4_K_S | [KaraKaraWitch-SteyrCannon-Qwen2.5-72b-Q4_K_S](https://huggingface.co/featherless-ai-quants/KaraKaraWitch-SteyrCannon-Qwen2.5-72b-GGUF/tree/main/KaraKaraWitch-SteyrCannon-Qwen2.5-72b-Q4_K_S) | 41856.02 MB (folder) |
| Q5_K_M | [KaraKaraWitch-SteyrCannon-Qwen2.5-72b-Q5_K_M](https://huggingface.co/featherless-ai-quants/KaraKaraWitch-SteyrCannon-Qwen2.5-72b-GGUF/tree/main/KaraKaraWitch-SteyrCannon-Qwen2.5-72b-Q5_K_M) | 51925.15 MB (folder) |
| Q5_K_S | [KaraKaraWitch-SteyrCannon-Qwen2.5-72b-Q5_K_S](https://huggingface.co/featherless-ai-quants/KaraKaraWitch-SteyrCannon-Qwen2.5-72b-GGUF/tree/main/KaraKaraWitch-SteyrCannon-Qwen2.5-72b-Q5_K_S) | 48995.15 MB (folder) |
| Q6_K | [KaraKaraWitch-SteyrCannon-Qwen2.5-72b-Q6_K](https://huggingface.co/featherless-ai-quants/KaraKaraWitch-SteyrCannon-Qwen2.5-72b-GGUF/tree/main/KaraKaraWitch-SteyrCannon-Qwen2.5-72b-Q6_K) | 61366.68 MB (folder) |
| Q8_0 | [KaraKaraWitch-SteyrCannon-Qwen2.5-72b-Q8_0](https://huggingface.co/featherless-ai-quants/KaraKaraWitch-SteyrCannon-Qwen2.5-72b-GGUF/tree/main/KaraKaraWitch-SteyrCannon-Qwen2.5-72b-Q8_0) | 73683.37 MB (folder) |
---
## ⚡ 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) |
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 -->
|
mradermacher/Mistral-7B-Discord-0.1-DPO-i1-GGUF | mradermacher | 2024-11-10T17:41:13Z | 12 | 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",
"imatrix"
]
| null | 2024-11-10T15:00:39Z | ---
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: nicoboss -->
weighted/imatrix quants of https://huggingface.co/BlouseJury/Mistral-7B-Discord-0.1-DPO
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-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-i1-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-i1-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-i1-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-i1-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-i1-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-i1-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-i1-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-i1-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-i1-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-i1-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.3 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-i1-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.i1-IQ3_S.gguf) | i1-IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-i1-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-i1-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-i1-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.9 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-i1-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-i1-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.2 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-i1-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.2 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-i1-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.2 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-i1-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.i1-Q4_0.gguf) | i1-Q4_0 | 4.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-i1-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-i1-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-i1-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-i1-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Discord-0.1-DPO-i1-GGUF/resolve/main/Mistral-7B-Discord-0.1-DPO.i1-Q6_K.gguf) | i1-Q6_K | 6.0 | 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 -->
|
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% |
|
jithuj12344321/whisper-small-en | jithuj12344321 | 2024-11-10T17:22:24Z | 76 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"en",
"dataset:kaggle/medical-speech-transcription-and-intent",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2024-11-09T19:43:25Z | ---
library_name: transformers
language:
- en
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- kaggle/medical-speech-transcription-and-intent
metrics:
- wer
model-index:
- name: Whisper Small En - Jithu J
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: medical-speech-transcription-and-intent
type: kaggle/medical-speech-transcription-and-intent
args: 'config: en, split: test'
metrics:
- name: Wer
type: wer
value: 3.9012226512226515
---
<!-- 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 En - Jithu J
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the medical-speech-transcription-and-intent dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0931
- Wer: 3.9012
## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 0.0165 | 3.3898 | 1000 | 0.0971 | 4.7860 |
| 0.0012 | 6.7797 | 2000 | 0.0905 | 4.1425 |
| 0.0001 | 10.1695 | 3000 | 0.0930 | 4.0138 |
| 0.0001 | 13.5593 | 4000 | 0.0931 | 3.9012 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu118
- Datasets 3.1.0
- Tokenizers 0.20.3
|
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 -->
|
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] |
lafarizo/code_translation_v2 | lafarizo | 2024-11-10T17:15:34Z | 141 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_bigcode",
"text-generation",
"code-translation",
"code-to-code",
"java",
"csharp",
"dataset:google/code_x_glue_cc_code_to_code_trans",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-11-08T08:39:09Z | ---
title: "Code Translation v2"
tags:
- code-translation
- code-to-code
- java
- csharp
library_name: "transformers"
datasets:
- google/code_x_glue_cc_code_to_code_trans
widget:
- text: "public class HelloWorld { public static void main(String[] args) { System.out.println(\"Hello, World!\"); } }"
---
# Code Translation v2
Code Translation from Java to C#
### Model Sources
- **Repository:** [bigcode/tiny_starcoder_py](https://huggingface.co/bigcode/tiny_starcoder_py)
### Dataset
- **Repository:** [google/code_x_glue_cc_code_to_code_trans](https://huggingface.co/datasets/google/code_x_glue_cc_code_to_code_trans)
### Testing Data
- [Testing Data](https://huggingface.co/datasets/google/code_x_glue_cc_code_to_code_trans/viewer/default/test)
|
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
```
|
AJMALm/Gemma-2-9b-it-chat-doctor | AJMALm | 2024-11-10T17:10:31Z | 89 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-11-09T17:48:43Z | ---
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/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"> [
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</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"> [
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</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>
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</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)).
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</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>
<p align="right"> [
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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.
<p align="right"> [
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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) |
<p align="right"> [
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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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</p>
### 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
|
gayfortay13/Andy | gayfortay13 | 2024-11-10T16:59:49Z | 15 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
]
| text-to-image | 2024-11-10T16:58:23Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: >-
ActorBF , ig selfie, photo, from above, in common, in his room, in the dark,
normal style, photo
output:
url: images/ActorBF , ig selfie, photo, from above, in comm....png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: Andrew
---
# Andrew G
<Gallery />
## Trigger words
You should use `Andrew` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/gayfortay13/Andy/tree/main) them in the Files & versions tab.
|
Siheng99/Qwen2.5-7B-Instruct-SEALONG | Siheng99 | 2024-11-10T16:54:33Z | 7 | 2 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-11-10T16:39:30Z | ---
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]
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## Model Card Contact
[More Information Needed] |
FINGU-AI/Qwen2.5-32B-Lora-HQ-e-4 | FINGU-AI | 2024-11-10T16:50:57Z | 5 | 0 | peft | [
"peft",
"safetensors",
"en",
"ko",
"zh",
"pt",
"ja",
"uz",
"tl",
"th",
"vi",
"id",
"base_model:Qwen/Qwen2.5-32B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-32B-Instruct",
"license:mit",
"region:us"
]
| null | 2024-11-10T16:49:43Z | ---
base_model: Qwen/Qwen2.5-32B-Instruct
library_name: peft
license: mit
language:
- en
- ko
- zh
- pt
- ja
- uz
- tl
- th
- vi
- id
---
# FINGU-AI/Qwen2.5-32B-Lora-HQ-e-4
## Overview
`FINGU-AI/Qwen2.5-32B-Lora-HQ-e-4` is a powerful causal language model designed for a variety of natural language processing (NLP) tasks, including machine translation, text generation, and chat-based applications. This model is particularly useful for translating between Korean and Uzbek, as well as supporting other custom NLP tasks through flexible input.
## Model Details
- **Model ID**: `FINGU-AI/Qwen2.5-32B-Lora-HQ-e-4`
- **Architecture**: Causal Language Model (LM)
- **Parameters**: 32 billion
- **Precision**: Torch BF16 for efficient GPU memory usage
- **Attention**: SDPA (Scaled Dot-Product Attention)
- **Primary Use Case**: Translation (e.g., Korean to Uzbek), text generation, and dialogue systems.
## Example Usage
### Installation
Make sure to install the required packages:
```bash
pip install torch transformers
```
### Loading the Model
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Model and Tokenizer
model_id = 'FINGU-AI/Qwen2.5-32B-Lora-HQ-e-4'
model = AutoModelForCausalLM.from_pretrained(model_id, attn_implementation="sdpa", torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(model_id)
model.to('cuda')
# Input Messages for Translation
messages = [
{"role": "system", "content": "translate korean to Uzbek"},
{"role": "user", "content": """새로운 은행 계좌를 개설하는 절차는 다음과 같습니다:
1. 계좌 개설 목적과 신분 확인을 위한 서류 제출
2. 서류 검토 과정을 거치는 것
3. 고객님의 신원 확인 절차를 진행하는 것
4. 모든 절차가 완료되면 계좌 개설이 가능합니다.
계좌 개설을 원하시는 경우, 신분증과 함께 방문해 주시면 됩니다.
"""},
]
# Tokenize and Generate Response
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to('cuda')
outputs = model.generate(
input_ids,
max_new_tokens=500,
do_sample=True,
)
# Decode and Print the Translation
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))
``` |
SKNahin/functionary-medium-v3.1-fine-llamafactory | SKNahin | 2024-11-10T16:37:27Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"feature-extraction",
"llama-factory",
"full",
"generated_from_trainer",
"custom_code",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| feature-extraction | 2024-11-10T16:17:32Z | ---
library_name: transformers
base_model: functionary-small-v3.1
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: functionary-medium-v3.1-fine-llamafactory
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. -->
# functionary-medium-v3.1-fine-llamafactory
This model is a fine-tuned version of [functionary-small-v3.1](https://huggingface.co/functionary-small-v3.1) on the sample_1 dataset.
## 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: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- total_eval_batch_size: 32
- 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: cosine
- lr_scheduler_warmup_ratio: 0.001
- num_epochs: 2.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
PriyHF/brand_product_recog | PriyHF | 2024-11-10T16:27:20Z | 106 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-11-10T16:24:43Z | ---
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
|
Criser2013/NER-finetuning-XML-RoBERTa-BIOBERT | Criser2013 | 2024-11-10T16:20:20Z | 25 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:biobert_json",
"base_model:raulgdp/xml-roberta-large-finetuned-ner",
"base_model:finetune:raulgdp/xml-roberta-large-finetuned-ner",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2024-11-09T15:08:20Z | ---
library_name: transformers
base_model: raulgdp/xml-roberta-large-finetuned-ner
tags:
- generated_from_trainer
datasets:
- biobert_json
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NER-finetuning-XML-RoBERTa-BIOBERT
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: biobert_json
type: biobert_json
config: Biobert_json
split: validation
args: Biobert_json
metrics:
- name: Precision
type: precision
value: 0.9497881598534296
- name: Recall
type: recall
value: 0.9714235521461615
- name: F1
type: f1
value: 0.9604840343919173
- name: Accuracy
type: accuracy
value: 0.981362755330252
---
<!-- 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. -->
# NER-finetuning-XML-RoBERTa-BIOBERT
This model is a fine-tuned version of [raulgdp/xml-roberta-large-finetuned-ner](https://huggingface.co/raulgdp/xml-roberta-large-finetuned-ner) on the biobert_json dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0946
- Precision: 0.9498
- Recall: 0.9714
- F1: 0.9605
- Accuracy: 0.9814
## 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: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1306 | 1.0 | 1224 | 0.1013 | 0.9299 | 0.9609 | 0.9451 | 0.9735 |
| 0.0996 | 2.0 | 2448 | 0.0932 | 0.9383 | 0.9656 | 0.9517 | 0.9777 |
| 0.0608 | 3.0 | 3672 | 0.0865 | 0.9493 | 0.9720 | 0.9605 | 0.9813 |
| 0.0445 | 4.0 | 4896 | 0.0927 | 0.9531 | 0.9729 | 0.9629 | 0.9819 |
| 0.0327 | 5.0 | 6120 | 0.0946 | 0.9498 | 0.9714 | 0.9605 | 0.9814 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1
|
martinsinnona/plotqa_simple_5_2k | martinsinnona | 2024-11-10T16:04:18Z | 48 | 0 | transformers | [
"transformers",
"safetensors",
"pix2struct",
"image-text-to-text",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| image-text-to-text | 2024-11-09T20:49:54Z | ---
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]
|
waloneai/mmac | waloneai | 2024-11-10T16:00:23Z | 261 | 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 | 2024-11-10T16:00:19Z | ---
tags:
- flux
- text-to-image
- lora
- diffusers
- fal
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: mmac
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
---
# mmac
<Gallery />
## Model description
## Trigger words
You should use `mmac` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/shweaung/mmac/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).
|
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
|
yam3333/mBART_Finetune_NagarGPT | yam3333 | 2024-11-10T15:55:45Z | 104 | 0 | transformers | [
"transformers",
"safetensors",
"mbart",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2024-11-10T15:54:33Z | ---
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]
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[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:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
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 -->
|
mradermacher/opus-v0-70b-GGUF | mradermacher | 2024-11-10T15:47:41Z | 19 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:dreamgen/opus-v0-70b",
"base_model:quantized:dreamgen/opus-v0-70b",
"endpoints_compatible",
"region:us"
]
| null | 2024-11-06T08:17:40Z | ---
base_model: dreamgen/opus-v0-70b
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/dreamgen/opus-v0-70b
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/opus-v0-70b-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/opus-v0-70b-GGUF/resolve/main/opus-v0-70b.Q2_K.gguf) | Q2_K | 25.6 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v0-70b-GGUF/resolve/main/opus-v0-70b.Q3_K_S.gguf) | Q3_K_S | 30.0 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v0-70b-GGUF/resolve/main/opus-v0-70b.Q3_K_M.gguf) | Q3_K_M | 33.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/opus-v0-70b-GGUF/resolve/main/opus-v0-70b.Q3_K_L.gguf) | Q3_K_L | 36.2 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v0-70b-GGUF/resolve/main/opus-v0-70b.IQ4_XS.gguf) | IQ4_XS | 37.3 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v0-70b-GGUF/resolve/main/opus-v0-70b.Q4_K_S.gguf) | Q4_K_S | 39.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/opus-v0-70b-GGUF/resolve/main/opus-v0-70b.Q4_K_M.gguf) | Q4_K_M | 41.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/opus-v0-70b-GGUF/resolve/main/opus-v0-70b.Q5_K_S.gguf) | Q5_K_S | 47.6 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v0-70b-GGUF/resolve/main/opus-v0-70b.Q5_K_M.gguf) | Q5_K_M | 48.9 | |
| [PART 1](https://huggingface.co/mradermacher/opus-v0-70b-GGUF/resolve/main/opus-v0-70b.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/opus-v0-70b-GGUF/resolve/main/opus-v0-70b.Q6_K.gguf.part2of2) | Q6_K | 56.7 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/opus-v0-70b-GGUF/resolve/main/opus-v0-70b.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/opus-v0-70b-GGUF/resolve/main/opus-v0-70b.Q8_0.gguf.part2of2) | Q8_0 | 73.4 | 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. 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/opus-v0-70b-i1-GGUF | mradermacher | 2024-11-10T15:47:41Z | 76 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:dreamgen/opus-v0-70b",
"base_model:quantized:dreamgen/opus-v0-70b",
"endpoints_compatible",
"region:us",
"imatrix"
]
| null | 2024-11-10T11:46:52Z | ---
base_model: dreamgen/opus-v0-70b
language:
- en
library_name: transformers
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/dreamgen/opus-v0-70b
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/opus-v0-70b-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/opus-v0-70b-i1-GGUF/resolve/main/opus-v0-70b.i1-IQ1_S.gguf) | i1-IQ1_S | 14.6 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/opus-v0-70b-i1-GGUF/resolve/main/opus-v0-70b.i1-IQ1_M.gguf) | i1-IQ1_M | 16.0 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/opus-v0-70b-i1-GGUF/resolve/main/opus-v0-70b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 18.4 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v0-70b-i1-GGUF/resolve/main/opus-v0-70b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 20.4 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v0-70b-i1-GGUF/resolve/main/opus-v0-70b.i1-IQ2_S.gguf) | i1-IQ2_S | 21.5 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v0-70b-i1-GGUF/resolve/main/opus-v0-70b.i1-IQ2_M.gguf) | i1-IQ2_M | 23.3 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v0-70b-i1-GGUF/resolve/main/opus-v0-70b.i1-Q2_K.gguf) | i1-Q2_K | 25.6 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/opus-v0-70b-i1-GGUF/resolve/main/opus-v0-70b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 26.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/opus-v0-70b-i1-GGUF/resolve/main/opus-v0-70b.i1-IQ3_XS.gguf) | i1-IQ3_XS | 28.4 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v0-70b-i1-GGUF/resolve/main/opus-v0-70b.i1-IQ3_S.gguf) | i1-IQ3_S | 30.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/opus-v0-70b-i1-GGUF/resolve/main/opus-v0-70b.i1-Q3_K_S.gguf) | i1-Q3_K_S | 30.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/opus-v0-70b-i1-GGUF/resolve/main/opus-v0-70b.i1-IQ3_M.gguf) | i1-IQ3_M | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v0-70b-i1-GGUF/resolve/main/opus-v0-70b.i1-Q3_K_M.gguf) | i1-Q3_K_M | 33.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/opus-v0-70b-i1-GGUF/resolve/main/opus-v0-70b.i1-Q3_K_L.gguf) | i1-Q3_K_L | 36.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/opus-v0-70b-i1-GGUF/resolve/main/opus-v0-70b.i1-IQ4_XS.gguf) | i1-IQ4_XS | 36.9 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v0-70b-i1-GGUF/resolve/main/opus-v0-70b.i1-Q4_0.gguf) | i1-Q4_0 | 39.1 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/opus-v0-70b-i1-GGUF/resolve/main/opus-v0-70b.i1-Q4_K_S.gguf) | i1-Q4_K_S | 39.3 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/opus-v0-70b-i1-GGUF/resolve/main/opus-v0-70b.i1-Q4_K_M.gguf) | i1-Q4_K_M | 41.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/opus-v0-70b-i1-GGUF/resolve/main/opus-v0-70b.i1-Q5_K_S.gguf) | i1-Q5_K_S | 47.6 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v0-70b-i1-GGUF/resolve/main/opus-v0-70b.i1-Q5_K_M.gguf) | i1-Q5_K_M | 48.9 | |
| [PART 1](https://huggingface.co/mradermacher/opus-v0-70b-i1-GGUF/resolve/main/opus-v0-70b.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/opus-v0-70b-i1-GGUF/resolve/main/opus-v0-70b.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 56.7 | 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 -->
|
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)
|
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
```
|
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}
}
```
|
sun-s/Pyramids_Training | sun-s | 2024-11-10T15:14:59Z | 8 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
]
| reinforcement-learning | 2024-11-10T14:43:35Z | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: sun-s/Pyramids_Training
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
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).
- **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] |
AshishFugare/blipImprov1 | AshishFugare | 2024-11-10T15:09:25Z | 10 | 0 | null | [
"pytorch",
"safetensors",
"blip-2",
"vision",
"image-to-text",
"image-captioning",
"visual-question-answering",
"en",
"arxiv:2301.12597",
"license:mit",
"region:us"
]
| image-to-text | 2024-11-10T15:09:25Z | ---
language: en
license: mit
tags:
- vision
- image-to-text
- image-captioning
- visual-question-answering
pipeline_tag: image-to-text
---
# BLIP-2, OPT-2.7b, pre-trained only
BLIP-2 model, leveraging [OPT-2.7b](https://huggingface.co/facebook/opt-2.7b) (a large language model with 2.7 billion parameters).
It was introduced in the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/abs/2301.12597) by Li et al. and first released in [this repository](https://github.com/salesforce/LAVIS/tree/main/projects/blip2).
Disclaimer: The team releasing BLIP-2 did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
BLIP-2 consists of 3 models: a CLIP-like image encoder, a Querying Transformer (Q-Former) and a large language model.
The authors initialize the weights of the image encoder and large language model from pre-trained checkpoints and keep them frozen
while training the Querying Transformer, which is a BERT-like Transformer encoder that maps a set of "query tokens" to query embeddings,
which bridge the gap between the embedding space of the image encoder and the large language model.
The goal for the model is simply to predict the next text token, giving the query embeddings and the previous text.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/blip2_architecture.jpg"
alt="drawing" width="600"/>
This allows the model to be used for tasks like:
- image captioning
- visual question answering (VQA)
- chat-like conversations by feeding the image and the previous conversation as prompt to the model
## Direct Use and Downstream Use
You can use the raw model for conditional text generation given an image and optional text. See the [model hub](https://huggingface.co/models?search=Salesforce/blip) to look for
fine-tuned versions on a task that interests you.
## Bias, Risks, Limitations, and Ethical Considerations
BLIP2-OPT uses off-the-shelf OPT as the language model. It inherits the same risks and limitations as mentioned in Meta's model card.
> Like other large language models for which the diversity (or lack thereof) of training
> data induces downstream impact on the quality of our model, OPT-175B has limitations in terms
> of bias and safety. OPT-175B can also have quality issues in terms of generation diversity and
> hallucination. In general, OPT-175B is not immune from the plethora of issues that plague modern
> large language models.
>
BLIP2 is fine-tuned on image-text datasets (e.g. [LAION](https://laion.ai/blog/laion-400-open-dataset/) ) collected from the internet. As a result the model itself is potentially vulnerable to generating equivalently inappropriate content or replicating inherent biases in the underlying data.
BLIP2 has not been tested in real world applications. It should not be directly deployed in any applications. Researchers should first carefully assess the safety and fairness of the model in relation to the specific context they’re being deployed within.
### How to use
For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/blip-2#transformers.Blip2ForConditionalGeneration.forward.example).
### Memory requirements
The memory requirements differ based on the precision one uses. One can use 4-bit inference using [Bitsandbytes](https://huggingface.co/blog/4bit-transformers-bitsandbytes), which greatly reduce the memory requirements.
| dtype | Largest Layer or Residual Group | Total Size | Training using Adam |
|-------------------|---------------------------------|------------|----------------------|
| float32 | 490.94 MB | 14.43 GB | 57.72 GB |
| float16/bfloat16 | 245.47 MB | 7.21 GB | 28.86 GB |
| int8 | 122.73 MB | 3.61 GB | 14.43 GB |
| int4 | 61.37 MB | 1.8 GB | 7.21 GB |
#### Running the model on CPU
<details>
<summary> Click to expand </summary>
```python
import requests
from PIL import Image
from transformers import Blip2Processor, Blip2ForConditionalGeneration
processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b")
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
question = "how many dogs are in the picture?"
inputs = processor(raw_image, question, return_tensors="pt")
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True).strip())
```
</details>
#### Running the model on GPU
##### In full precision
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate
import requests
from PIL import Image
from transformers import Blip2Processor, Blip2ForConditionalGeneration
processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", device_map="auto")
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
question = "how many dogs are in the picture?"
inputs = processor(raw_image, question, return_tensors="pt").to("cuda")
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True).strip())
```
</details>
##### In half precision (`float16`)
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate
import torch
import requests
from PIL import Image
from transformers import Blip2Processor, Blip2ForConditionalGeneration
processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16, device_map="auto")
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
question = "how many dogs are in the picture?"
inputs = processor(raw_image, question, return_tensors="pt").to("cuda", torch.float16)
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True).strip())
```
</details>
##### In 8-bit precision (`int8`)
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate bitsandbytes
import torch
import requests
from PIL import Image
from transformers import Blip2Processor, Blip2ForConditionalGeneration
processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", load_in_8bit=True, device_map="auto")
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
question = "how many dogs are in the picture?"
inputs = processor(raw_image, question, return_tensors="pt").to("cuda", torch.float16)
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True).strip())
```
</details> |
mradermacher/gemma-2b-openhermes-i1-GGUF | mradermacher | 2024-11-10T15:02:08Z | 45 | 0 | 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-2b-openhermes",
"base_model:quantized:abideen/gemma-2b-openhermes",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
]
| null | 2024-11-10T14:14:22Z | ---
base_model: abideen/gemma-2b-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-2b-openhermes
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/gemma-2b-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-2b-openhermes-i1-GGUF/resolve/main/gemma-2b-openhermes.i1-IQ1_S.gguf) | i1-IQ1_S | 0.9 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/gemma-2b-openhermes-i1-GGUF/resolve/main/gemma-2b-openhermes.i1-IQ1_M.gguf) | i1-IQ1_M | 0.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/gemma-2b-openhermes-i1-GGUF/resolve/main/gemma-2b-openhermes.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-2b-openhermes-i1-GGUF/resolve/main/gemma-2b-openhermes.i1-IQ2_XS.gguf) | i1-IQ2_XS | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-2b-openhermes-i1-GGUF/resolve/main/gemma-2b-openhermes.i1-IQ2_S.gguf) | i1-IQ2_S | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-2b-openhermes-i1-GGUF/resolve/main/gemma-2b-openhermes.i1-IQ2_M.gguf) | i1-IQ2_M | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-2b-openhermes-i1-GGUF/resolve/main/gemma-2b-openhermes.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 1.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-2b-openhermes-i1-GGUF/resolve/main/gemma-2b-openhermes.i1-Q2_K.gguf) | i1-Q2_K | 1.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/gemma-2b-openhermes-i1-GGUF/resolve/main/gemma-2b-openhermes.i1-IQ3_XS.gguf) | i1-IQ3_XS | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-2b-openhermes-i1-GGUF/resolve/main/gemma-2b-openhermes.i1-Q3_K_S.gguf) | i1-Q3_K_S | 1.4 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/gemma-2b-openhermes-i1-GGUF/resolve/main/gemma-2b-openhermes.i1-IQ3_S.gguf) | i1-IQ3_S | 1.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/gemma-2b-openhermes-i1-GGUF/resolve/main/gemma-2b-openhermes.i1-IQ3_M.gguf) | i1-IQ3_M | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-2b-openhermes-i1-GGUF/resolve/main/gemma-2b-openhermes.i1-Q3_K_M.gguf) | i1-Q3_K_M | 1.5 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/gemma-2b-openhermes-i1-GGUF/resolve/main/gemma-2b-openhermes.i1-Q3_K_L.gguf) | i1-Q3_K_L | 1.6 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/gemma-2b-openhermes-i1-GGUF/resolve/main/gemma-2b-openhermes.i1-IQ4_XS.gguf) | i1-IQ4_XS | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-2b-openhermes-i1-GGUF/resolve/main/gemma-2b-openhermes.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 1.7 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-2b-openhermes-i1-GGUF/resolve/main/gemma-2b-openhermes.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 1.7 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-2b-openhermes-i1-GGUF/resolve/main/gemma-2b-openhermes.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 1.7 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-2b-openhermes-i1-GGUF/resolve/main/gemma-2b-openhermes.i1-Q4_0.gguf) | i1-Q4_0 | 1.7 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-2b-openhermes-i1-GGUF/resolve/main/gemma-2b-openhermes.i1-Q4_K_S.gguf) | i1-Q4_K_S | 1.7 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-2b-openhermes-i1-GGUF/resolve/main/gemma-2b-openhermes.i1-Q4_K_M.gguf) | i1-Q4_K_M | 1.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gemma-2b-openhermes-i1-GGUF/resolve/main/gemma-2b-openhermes.i1-Q5_K_S.gguf) | i1-Q5_K_S | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-2b-openhermes-i1-GGUF/resolve/main/gemma-2b-openhermes.i1-Q5_K_M.gguf) | i1-Q5_K_M | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-2b-openhermes-i1-GGUF/resolve/main/gemma-2b-openhermes.i1-Q6_K.gguf) | i1-Q6_K | 2.2 | 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 -->
|
shravankumar147/model | shravankumar147 | 2024-11-10T14:59:42Z | 23 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2024-11-10T14:57:29Z | ---
base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
---
# Uploaded model
- **Developed by:** shravankumar147
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-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)
|
Leonuraht/IMDBert | Leonuraht | 2024-11-10T14:58:23Z | 146 | 1 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"torch",
"code",
"en",
"dataset:stanfordnlp/imdb",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-11-09T13:49:04Z | ---
datasets:
- stanfordnlp/imdb
language:
- en
base_model:
- distilbert/distilbert-base-uncased
tags:
- torch
- code
library_name: transformers
pipeline_tag: text-classification
metrics:
- accuracy
---
# DistilBERT Fine-Tuned on IMDB Sentiment Analysis
This model is a fine-tuned version of `DistilBERT` for sentiment analysis on the IMDB movie reviews dataset. It classifies movie reviews into two categories: positive and negative sentiments.
## Model Details
### Model Description
This model has been fine-tuned on the IMDB dataset, which contains movie reviews labeled with sentiments: `positive` or `negative`. The model is based on the `DistilBERT` architecture, which is a lighter, more efficient variant of BERT, offering faster inference without significantly sacrificing accuracy.
- **Developed by:** Leonuraht/Scilineo
- **Model type:** Transformer-based model for text classification (sentiment analysis)
- **Language(s) (NLP):** English
- **Finetuned from model :** distilbert-base-uncased
## Uses
### Direct Use
This model is directly usable for sentiment analysis tasks. It predicts the sentiment of text by classifying it as either "positive" or "negative".
### Downstream Use [optional]
This model can be further fine-tuned for other text classification tasks or integrated into larger applications where sentiment analysis is required.
### Out-of-Scope Use
This model is not intended for multilingual sentiment analysis or for handling text outside of movie reviews. It may not perform well on domains with vastly different vocabularies or sentiment expression styles.
## Bias, Risks, and Limitations
The model has been trained on the IMDB movie reviews dataset, and as such, it may exhibit biases inherent in the data (e.g., biases in sentiment based on genre, culture, or language). It is important to be mindful of these limitations when using the model in real-world applications.
### Recommendations
Users should be aware of the model's biases and limitations. It is recommended to further fine-tune the model with a diverse dataset if it is to be used in domains beyond movie reviews.
## How to Get Started with the Model
To use the model for sentiment analysis, you can load it via the Hugging Face `transformers` library. Here's an example:
```python
from transformers import pipeline
# Load the fine-tuned model from Hugging Face
model = "Leonuraht/IMDBert"
classifier = pipeline("sentiment-analysis", model=model)
# Test the model with a sample text
result = classifier("This movie was amazing!")
print(result) # Outputs: [{'label': 'POSITIVE' }] |
yashmarathe/llama-3.1-mod | yashmarathe | 2024-11-10T14:57:18Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-11-10T14:46:32Z | ---
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] |
alidenewade/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan | alidenewade | 2024-11-10T14:49:01Z | 160 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"audio-spectrogram-transformer",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:MIT/ast-finetuned-audioset-10-10-0.4593",
"base_model:finetune:MIT/ast-finetuned-audioset-10-10-0.4593",
"license:bsd-3-clause",
"model-index",
"endpoints_compatible",
"region:us"
]
| audio-classification | 2024-11-10T14:17:01Z | ---
library_name: transformers
license: bsd-3-clause
base_model: MIT/ast-finetuned-audioset-10-10-0.4593
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.91
---
<!-- 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. -->
# ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan
This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5608
- Accuracy: 0.91
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7343 | 1.0 | 100 | 1.0826 | 0.62 |
| 0.6206 | 2.0 | 200 | 0.6780 | 0.75 |
| 0.3899 | 3.0 | 300 | 0.7010 | 0.81 |
| 0.087 | 4.0 | 400 | 0.6710 | 0.84 |
| 0.004 | 5.0 | 500 | 0.5797 | 0.89 |
| 0.0009 | 6.0 | 600 | 0.7082 | 0.87 |
| 0.0001 | 7.0 | 700 | 0.5387 | 0.91 |
| 0.0001 | 8.0 | 800 | 0.5515 | 0.915 |
| 0.0001 | 9.0 | 900 | 0.5586 | 0.91 |
| 0.0001 | 10.0 | 1000 | 0.5608 | 0.91 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1
|
bogoconic1/Qwen2.5-14B-AWQ-exp1 | bogoconic1 | 2024-11-10T14:40:03Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-11-10T14:23:31Z | ---
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] |
mradermacher/opus-v0.5-70b-i1-GGUF | mradermacher | 2024-11-10T14:39:09Z | 151 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:dreamgen/opus-v0.5-70b",
"base_model:quantized:dreamgen/opus-v0.5-70b",
"endpoints_compatible",
"region:us",
"imatrix"
]
| null | 2024-11-10T11:01:45Z | ---
base_model: dreamgen/opus-v0.5-70b
language:
- en
library_name: transformers
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/dreamgen/opus-v0.5-70b
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/opus-v0.5-70b-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/opus-v0.5-70b-i1-GGUF/resolve/main/opus-v0.5-70b.i1-IQ1_S.gguf) | i1-IQ1_S | 14.6 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/opus-v0.5-70b-i1-GGUF/resolve/main/opus-v0.5-70b.i1-IQ1_M.gguf) | i1-IQ1_M | 16.0 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/opus-v0.5-70b-i1-GGUF/resolve/main/opus-v0.5-70b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 18.4 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v0.5-70b-i1-GGUF/resolve/main/opus-v0.5-70b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 20.4 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v0.5-70b-i1-GGUF/resolve/main/opus-v0.5-70b.i1-IQ2_S.gguf) | i1-IQ2_S | 21.5 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v0.5-70b-i1-GGUF/resolve/main/opus-v0.5-70b.i1-IQ2_M.gguf) | i1-IQ2_M | 23.3 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v0.5-70b-i1-GGUF/resolve/main/opus-v0.5-70b.i1-Q2_K.gguf) | i1-Q2_K | 25.6 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/opus-v0.5-70b-i1-GGUF/resolve/main/opus-v0.5-70b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 26.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/opus-v0.5-70b-i1-GGUF/resolve/main/opus-v0.5-70b.i1-IQ3_XS.gguf) | i1-IQ3_XS | 28.4 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v0.5-70b-i1-GGUF/resolve/main/opus-v0.5-70b.i1-IQ3_S.gguf) | i1-IQ3_S | 30.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/opus-v0.5-70b-i1-GGUF/resolve/main/opus-v0.5-70b.i1-Q3_K_S.gguf) | i1-Q3_K_S | 30.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/opus-v0.5-70b-i1-GGUF/resolve/main/opus-v0.5-70b.i1-IQ3_M.gguf) | i1-IQ3_M | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v0.5-70b-i1-GGUF/resolve/main/opus-v0.5-70b.i1-Q3_K_M.gguf) | i1-Q3_K_M | 33.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/opus-v0.5-70b-i1-GGUF/resolve/main/opus-v0.5-70b.i1-Q3_K_L.gguf) | i1-Q3_K_L | 36.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/opus-v0.5-70b-i1-GGUF/resolve/main/opus-v0.5-70b.i1-IQ4_XS.gguf) | i1-IQ4_XS | 36.9 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v0.5-70b-i1-GGUF/resolve/main/opus-v0.5-70b.i1-Q4_0.gguf) | i1-Q4_0 | 39.1 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/opus-v0.5-70b-i1-GGUF/resolve/main/opus-v0.5-70b.i1-Q4_K_S.gguf) | i1-Q4_K_S | 39.3 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/opus-v0.5-70b-i1-GGUF/resolve/main/opus-v0.5-70b.i1-Q4_K_M.gguf) | i1-Q4_K_M | 41.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/opus-v0.5-70b-i1-GGUF/resolve/main/opus-v0.5-70b.i1-Q5_K_S.gguf) | i1-Q5_K_S | 47.6 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v0.5-70b-i1-GGUF/resolve/main/opus-v0.5-70b.i1-Q5_K_M.gguf) | i1-Q5_K_M | 48.9 | |
| [PART 1](https://huggingface.co/mradermacher/opus-v0.5-70b-i1-GGUF/resolve/main/opus-v0.5-70b.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/opus-v0.5-70b-i1-GGUF/resolve/main/opus-v0.5-70b.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 56.7 | 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/opus-v0.5-70b-GGUF | mradermacher | 2024-11-10T14:39:09Z | 41 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:dreamgen/opus-v0.5-70b",
"base_model:quantized:dreamgen/opus-v0.5-70b",
"endpoints_compatible",
"region:us"
]
| null | 2024-11-06T07:26:11Z | ---
base_model: dreamgen/opus-v0.5-70b
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/dreamgen/opus-v0.5-70b
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/opus-v0.5-70b-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/opus-v0.5-70b-GGUF/resolve/main/opus-v0.5-70b.Q2_K.gguf) | Q2_K | 25.6 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v0.5-70b-GGUF/resolve/main/opus-v0.5-70b.Q3_K_S.gguf) | Q3_K_S | 30.0 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v0.5-70b-GGUF/resolve/main/opus-v0.5-70b.Q3_K_M.gguf) | Q3_K_M | 33.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/opus-v0.5-70b-GGUF/resolve/main/opus-v0.5-70b.Q3_K_L.gguf) | Q3_K_L | 36.2 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v0.5-70b-GGUF/resolve/main/opus-v0.5-70b.IQ4_XS.gguf) | IQ4_XS | 37.3 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v0.5-70b-GGUF/resolve/main/opus-v0.5-70b.Q4_K_S.gguf) | Q4_K_S | 39.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/opus-v0.5-70b-GGUF/resolve/main/opus-v0.5-70b.Q4_K_M.gguf) | Q4_K_M | 41.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/opus-v0.5-70b-GGUF/resolve/main/opus-v0.5-70b.Q5_K_S.gguf) | Q5_K_S | 47.6 | |
| [GGUF](https://huggingface.co/mradermacher/opus-v0.5-70b-GGUF/resolve/main/opus-v0.5-70b.Q5_K_M.gguf) | Q5_K_M | 48.9 | |
| [PART 1](https://huggingface.co/mradermacher/opus-v0.5-70b-GGUF/resolve/main/opus-v0.5-70b.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/opus-v0.5-70b-GGUF/resolve/main/opus-v0.5-70b.Q6_K.gguf.part2of2) | Q6_K | 56.7 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/opus-v0.5-70b-GGUF/resolve/main/opus-v0.5-70b.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/opus-v0.5-70b-GGUF/resolve/main/opus-v0.5-70b.Q8_0.gguf.part2of2) | Q8_0 | 73.4 | 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. 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 -->
|
cuongdev/2nguoi-4000 | cuongdev | 2024-11-10T14:25:05Z | 31 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2024-11-10T14:21:23Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### 2nguoi-4000 Dreambooth model trained by cuongdev with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
win10/Qwen2.5-Math-12.3B-Instruct | win10 | 2024-11-10T14:21:37Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:Qwen/Qwen2.5-Math-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-Math-7B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-11-10T14:16:43Z | ---
base_model:
- Qwen/Qwen2.5-Math-7B-Instruct
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 passthrough merge method.
### Models Merged
The following models were included in the merge:
* [Qwen/Qwen2.5-Math-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
dtype: bfloat16
merge_method: passthrough
slices:
- sources:
- layer_range: [0, 8]
model: Qwen/Qwen2.5-Math-7B-Instruct
- sources:
- layer_range: [4, 12]
model: Qwen/Qwen2.5-Math-7B-Instruct
- sources:
- layer_range: [8, 16]
model: Qwen/Qwen2.5-Math-7B-Instruct
- sources:
- layer_range: [12, 20]
model: Qwen/Qwen2.5-Math-7B-Instruct
- sources:
- layer_range: [16, 24]
model: Qwen/Qwen2.5-Math-7B-Instruct
- sources:
- layer_range: [20, 28]
model: Qwen/Qwen2.5-Math-7B-Instruct
```
|
kritsadaK/UltraInteract-Llama-FT | kritsadaK | 2024-11-10T13:58:09Z | 7 | 0 | null | [
"safetensors",
"llama",
"4-bit",
"bitsandbytes",
"region:us"
]
| null | 2024-11-10T13:36:30Z | ---
{}
---
# UltraInteract-Llama-FT
This model is a fine-tuned version of Llama-2 using the UltraInteract dataset. It has been trained to handle interactive conversational tasks with improved accuracy and contextual understanding.
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("kritsadaK/UltraInteract-Llama-FT")
model = AutoModelForCausalLM.from_pretrained("kritsadaK/UltraInteract-Llama-FT")
input_text = "typing your prompt here"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Details
Dataset: UltraInteract
Training Parameters: 4-bit quantization with LoRA
|
cuongdev/2nguoi-2000 | cuongdev | 2024-11-10T13:44:51Z | 29 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2024-11-10T13:39:36Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### 2nguoi-2000 Dreambooth model trained by cuongdev with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
oodeh/ods-ci-llama-r64-a16-epoch-8-merged-model | oodeh | 2024-11-10T13:39:16Z | 75 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit",
"base_model:quantized:unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
]
| text-generation | 2024-11-10T13:36:02Z | ---
base_model: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** oodeh
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Meta-Llama-3.1-8B-Instruct-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)
|
snagmin/xlm-roberta-base-finetuned-panx-de-fr | snagmin | 2024-11-10T13:37:04Z | 124 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"token-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"
]
| token-classification | 2024-11-10T13:24:40Z | ---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
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. -->
# xlm-roberta-base-finetuned-panx-de-fr
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: 0.1639
- F1: 0.8591
## 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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2836 | 1.0 | 715 | 0.1859 | 0.8212 |
| 0.1484 | 2.0 | 1430 | 0.1632 | 0.8487 |
| 0.0953 | 3.0 | 2145 | 0.1639 | 0.8591 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1
|
fancf/bert-base-sst2 | fancf | 2024-11-10T13:34:16Z | 105 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2024-11-10T13:33:40Z | ---
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] |
ehzoah/Llama-3.2-1B-sft-full | ehzoah | 2024-11-10T13:33:57Z | 144 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"conversational",
"dataset:HuggingFaceH4/ultrachat_200k",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:finetune:meta-llama/Llama-3.2-1B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-11-08T15:55:21Z | ---
library_name: transformers
base_model: meta-llama/Llama-3.2-1B
tags:
- alignment-handbook
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrachat_200k
model-index:
- name: Llama-3.2-1B-sft-full
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-3.2-1B-sft-full
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on the HuggingFaceH4/ultrachat_200k dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2660
## 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: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2488 | 1.0 | 951 | 1.2660 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
|
archit11/token_classification_model | archit11 | 2024-11-10T13:33:04Z | 54 | 0 | transformers | [
"transformers",
"safetensors",
"span-marker",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-11-10T13:32:42Z | ---
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] |
oodeh/ods-ci-mistral-r64-a16-epoch-19-merged-model | oodeh | 2024-11-10T13:32:32Z | 75 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"base_model:quantized:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
]
| text-generation | 2024-11-10T13:29:02Z | ---
base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** oodeh
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.3-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)
|
mradermacher/d-Qwen1.5-0.5B-i1-GGUF | mradermacher | 2024-11-10T13:20:10Z | 14 | 0 | transformers | [
"transformers",
"gguf",
"qwen2",
"en",
"dataset:EleutherAI/the_pile_deduplicated",
"base_model:aloobun/d-Qwen1.5-0.5B",
"base_model:quantized:aloobun/d-Qwen1.5-0.5B",
"license:other",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
]
| null | 2024-11-10T13:10:53Z | ---
base_model: aloobun/d-Qwen1.5-0.5B
datasets:
- EleutherAI/the_pile_deduplicated
language:
- en
library_name: transformers
license: other
license_link: https://huggingface.co/Qwen/Qwen1.5-0.5B/blob/main/LICENSE
license_name: tongyi-qianwen-research
quantized_by: mradermacher
tags:
- qwen2
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/aloobun/d-Qwen1.5-0.5B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/d-Qwen1.5-0.5B-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/d-Qwen1.5-0.5B-i1-GGUF/resolve/main/d-Qwen1.5-0.5B.i1-IQ1_S.gguf) | i1-IQ1_S | 0.3 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/d-Qwen1.5-0.5B-i1-GGUF/resolve/main/d-Qwen1.5-0.5B.i1-IQ1_M.gguf) | i1-IQ1_M | 0.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/d-Qwen1.5-0.5B-i1-GGUF/resolve/main/d-Qwen1.5-0.5B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/d-Qwen1.5-0.5B-i1-GGUF/resolve/main/d-Qwen1.5-0.5B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/d-Qwen1.5-0.5B-i1-GGUF/resolve/main/d-Qwen1.5-0.5B.i1-IQ2_S.gguf) | i1-IQ2_S | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/d-Qwen1.5-0.5B-i1-GGUF/resolve/main/d-Qwen1.5-0.5B.i1-IQ2_M.gguf) | i1-IQ2_M | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/d-Qwen1.5-0.5B-i1-GGUF/resolve/main/d-Qwen1.5-0.5B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/d-Qwen1.5-0.5B-i1-GGUF/resolve/main/d-Qwen1.5-0.5B.i1-Q2_K.gguf) | i1-Q2_K | 0.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/d-Qwen1.5-0.5B-i1-GGUF/resolve/main/d-Qwen1.5-0.5B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/d-Qwen1.5-0.5B-i1-GGUF/resolve/main/d-Qwen1.5-0.5B.i1-IQ3_S.gguf) | i1-IQ3_S | 0.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/d-Qwen1.5-0.5B-i1-GGUF/resolve/main/d-Qwen1.5-0.5B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.4 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/d-Qwen1.5-0.5B-i1-GGUF/resolve/main/d-Qwen1.5-0.5B.i1-IQ3_M.gguf) | i1-IQ3_M | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/d-Qwen1.5-0.5B-i1-GGUF/resolve/main/d-Qwen1.5-0.5B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/d-Qwen1.5-0.5B-i1-GGUF/resolve/main/d-Qwen1.5-0.5B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 0.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/d-Qwen1.5-0.5B-i1-GGUF/resolve/main/d-Qwen1.5-0.5B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/d-Qwen1.5-0.5B-i1-GGUF/resolve/main/d-Qwen1.5-0.5B.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 0.4 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/d-Qwen1.5-0.5B-i1-GGUF/resolve/main/d-Qwen1.5-0.5B.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 0.4 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/d-Qwen1.5-0.5B-i1-GGUF/resolve/main/d-Qwen1.5-0.5B.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 0.4 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/d-Qwen1.5-0.5B-i1-GGUF/resolve/main/d-Qwen1.5-0.5B.i1-Q4_0.gguf) | i1-Q4_0 | 0.4 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/d-Qwen1.5-0.5B-i1-GGUF/resolve/main/d-Qwen1.5-0.5B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 0.4 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/d-Qwen1.5-0.5B-i1-GGUF/resolve/main/d-Qwen1.5-0.5B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 0.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/d-Qwen1.5-0.5B-i1-GGUF/resolve/main/d-Qwen1.5-0.5B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/d-Qwen1.5-0.5B-i1-GGUF/resolve/main/d-Qwen1.5-0.5B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/d-Qwen1.5-0.5B-i1-GGUF/resolve/main/d-Qwen1.5-0.5B.i1-Q6_K.gguf) | i1-Q6_K | 0.5 | 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 -->
|
ManoloPueblo/LLM_MERGE_CC3 | ManoloPueblo | 2024-11-10T13:15:03Z | 9 | 1 | null | [
"safetensors",
"mistral",
"merge",
"mergekit",
"lazymergekit",
"llm-merge-cc3",
"mistral-7b",
"mistral-ft-optimized",
"neural-hermes",
"mistralai/Mistral-7B-v0.1",
"samir-fama/SamirGPT-v1",
"abacusai/Slerp-CM-mist-dpo",
"EmbeddedLLM/Mistral-7B-Merge-14-v0.2",
"license:apache-2.0",
"region:us"
]
| null | 2024-11-10T13:05:42Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- llm-merge-cc3
- mistral-7b
- mistral-ft-optimized
- neural-hermes
- mistralai/Mistral-7B-v0.1
- samir-fama/SamirGPT-v1
- abacusai/Slerp-CM-mist-dpo
- EmbeddedLLM/Mistral-7B-Merge-14-v0.2
---
# LLM_MERGE_CC3
LLM_MERGE_CC3 est une fusion des modèles suivants créée par ManoloPueblo utilisant [mergekit](https://github.com/cg123/mergekit):
* [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
* [samir-fama/SamirGPT-v1](https://huggingface.co/samir-fama/SamirGPT-v1)
* [abacusai/Slerp-CM-mist-dpo](https://huggingface.co/abacusai/Slerp-CM-mist-dpo)
* [EmbeddedLLM/Mistral-7B-Merge-14-v0.2](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.2)
## 🧩 Configuration de la fusion
```yaml
merge_method: dare
base_model: mistralai/Mistral-7B-v0.1
models:
- model: mistralai/Mistral-7B-v0.1
# No parameters necessary for base model
- model: samir-fama/SamirGPT-v1
parameters:
density: 0.53
weight: 0.4
- model: abacusai/Slerp-CM-mist-dpo
parameters:
density: 0.53
weight: 0.3
- model: EmbeddedLLM/Mistral-7B-Merge-14-v0.2
parameters:
density: 0.53
weight: 0.3
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
int8_mask: true
dtype: bfloat16
```
## Description
LLM_MERGE_CC3 est un modèle de langage créé par la fusion de trois variantes Mistral :
1. Mistral-7B-v0.1 - Le modèle de base Mistral (modèle de référence)
2. mistral-ft-optimized-1218 - Version optimisée par OpenPipe (poids: 0.5, densité: 0.5)
3. NeuralHermes-2.5-Mistral-7B - Version améliorée par MLabonne (poids: 0.3, densité: 0.5)
Cette fusion utilise la méthode "dare" avec normalisation et une précision float16 pour combiner les forces des trois modèles.
## Architecture
Le modèle conserve l'architecture de base de Mistral-7B tout en incorporant les améliorations des trois versions à travers une fusion pondérée. La méthode "ties" permet une fusion plus sophistiquée des poids des modèles.
## Paramètres de fusion
- Méthode de fusion : dare
- Normalisation : activée
- Type de données : float16
- Densités et poids :
* OpenPipe/mistral-ft-optimized-1218 : densité 0.5, poids 0.5
* NeuralHermes-2.5-Mistral-7B : densité 0.5, poids 0.3
## Utilisation
Ce modèle peut être utilisé avec la bibliothèque transformers de Hugging Face :
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("ManoloPueblo/LLM_MERGE_CC3")
model = AutoModelForCausalLM.from_pretrained("ManoloPueblo/LLM_MERGE_CC3")
```
## Modèles fusionnés
1. [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) - Modèle de base
2. [mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218) - Version optimisée
3. [NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) - Version améliorée
## Limitations
Comme pour tout modèle de langage, les utilisateurs doivent être conscients des biais potentiels et des limitations inhérentes aux modèles sources. Les performances peuvent varier selon les cas d'utilisation. |
mradermacher/Reyna-Mini-1.8B-v0.2-i1-GGUF | mradermacher | 2024-11-10T13:12:10Z | 160 | 0 | transformers | [
"transformers",
"gguf",
"chatml",
"finetune",
"gpt4",
"synthetic data",
"custom_code",
"qwen2",
"en",
"dataset:Locutusque/Hercules-v3.0",
"base_model:aloobun/Reyna-Mini-1.8B-v0.2",
"base_model:quantized:aloobun/Reyna-Mini-1.8B-v0.2",
"license:other",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
]
| null | 2024-11-10T12:39:41Z | ---
base_model: aloobun/Reyna-Mini-1.8B-v0.2
datasets:
- Locutusque/Hercules-v3.0
language:
- en
library_name: transformers
license: other
license_link: https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat/raw/main/LICENSE
license_name: tongyi-qianwen-research
quantized_by: mradermacher
tags:
- chatml
- finetune
- gpt4
- synthetic data
- custom_code
- qwen2
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/aloobun/Reyna-Mini-1.8B-v0.2
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-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/Reyna-Mini-1.8B-v0.2-i1-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.i1-IQ1_S.gguf) | i1-IQ1_S | 0.8 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-i1-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.i1-IQ1_M.gguf) | i1-IQ1_M | 0.8 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-i1-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-i1-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-i1-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.i1-IQ2_S.gguf) | i1-IQ2_S | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-i1-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.i1-IQ2_M.gguf) | i1-IQ2_M | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-i1-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.i1-Q2_K.gguf) | i1-Q2_K | 0.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-i1-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 1.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-i1-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.i1-IQ3_XS.gguf) | i1-IQ3_XS | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-i1-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.i1-IQ3_S.gguf) | i1-IQ3_S | 1.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-i1-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.i1-Q3_K_S.gguf) | i1-Q3_K_S | 1.1 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-i1-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.i1-IQ3_M.gguf) | i1-IQ3_M | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-i1-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.i1-Q3_K_M.gguf) | i1-Q3_K_M | 1.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-i1-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.i1-Q3_K_L.gguf) | i1-Q3_K_L | 1.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-i1-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.i1-IQ4_XS.gguf) | i1-IQ4_XS | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-i1-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 1.2 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-i1-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 1.2 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-i1-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 1.2 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-i1-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.i1-Q4_0.gguf) | i1-Q4_0 | 1.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-i1-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.i1-Q4_K_S.gguf) | i1-Q4_K_S | 1.3 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-i1-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.i1-Q4_K_M.gguf) | i1-Q4_K_M | 1.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-i1-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.i1-Q5_K_S.gguf) | i1-Q5_K_S | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-i1-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.i1-Q5_K_M.gguf) | i1-Q5_K_M | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-i1-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.i1-Q6_K.gguf) | i1-Q6_K | 1.7 | 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/Reyna-Mini-1.8B-v0.2-GGUF | mradermacher | 2024-11-10T13:12:09Z | 55 | 0 | transformers | [
"transformers",
"gguf",
"chatml",
"finetune",
"gpt4",
"synthetic data",
"custom_code",
"qwen2",
"en",
"dataset:Locutusque/Hercules-v3.0",
"base_model:aloobun/Reyna-Mini-1.8B-v0.2",
"base_model:quantized:aloobun/Reyna-Mini-1.8B-v0.2",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2024-11-07T08:16:43Z | ---
base_model: aloobun/Reyna-Mini-1.8B-v0.2
datasets:
- Locutusque/Hercules-v3.0
language:
- en
library_name: transformers
license: other
license_link: https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat/raw/main/LICENSE
license_name: tongyi-qianwen-research
quantized_by: mradermacher
tags:
- chatml
- finetune
- gpt4
- synthetic data
- custom_code
- qwen2
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/aloobun/Reyna-Mini-1.8B-v0.2
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-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/Reyna-Mini-1.8B-v0.2-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.Q2_K.gguf) | Q2_K | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.Q3_K_S.gguf) | Q3_K_S | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.Q3_K_M.gguf) | Q3_K_M | 1.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.Q3_K_L.gguf) | Q3_K_L | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.IQ4_XS.gguf) | IQ4_XS | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.Q4_0_4_4.gguf) | Q4_0_4_4 | 1.2 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.Q4_K_S.gguf) | Q4_K_S | 1.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.Q4_K_M.gguf) | Q4_K_M | 1.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.Q5_K_S.gguf) | Q5_K_S | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.Q5_K_M.gguf) | Q5_K_M | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.Q6_K.gguf) | Q6_K | 1.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.Q8_0.gguf) | Q8_0 | 2.1 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Reyna-Mini-1.8B-v0.2-GGUF/resolve/main/Reyna-Mini-1.8B-v0.2.f16.gguf) | f16 | 3.8 | 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 -->
|
almanach/camemberta-base | almanach | 2024-11-10T13:00:26Z | 275 | 10 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"deberta-v2",
"feature-extraction",
"deberta",
"deberta-v3",
"fr",
"dataset:ccnet",
"license:mit",
"region:us"
]
| feature-extraction | 2023-05-03T09:07:10Z | ---
license: mit
language: fr
datasets:
- ccnet
tags:
- deberta
- deberta-v3
inference: false
---
# CamemBERTa: A French language model based on DeBERTa V3
CamemBERTa, a French language model based on DeBERTa V3, which is a DeBerta V2 with ELECTRA style pretraining using the Replaced Token Detection (RTD) objective.
RTD uses a generator model, trained using the MLM objective, to replace masked tokens with plausible candidates, and a discriminator model trained to detect which tokens were replaced by the generator.
Usually the generator and discriminator share the same embedding matrix, but the authors of DeBERTa V3 propose a new technique to disentagle the gradients of the shared embedding between the generator and discriminator called gradient-disentangled embedding sharing (GDES)
*This the first publicly available implementation of DeBERTa V3, and the first publicly DeBERTaV3 model outside of the original Microsoft release.*
Preprint Paper: https://inria.hal.science/hal-03963729/
Pre-training Code: https://gitlab.inria.fr/almanach/CamemBERTa
## How to use CamemBERTa
Our pretrained weights are available on the HuggingFace model hub, you can load them using the following code:
```python
from transformers import AutoTokenizer, AutoModel, AutoModelForMaskedLM
CamemBERTa = AutoModel.from_pretrained("almanach/camemberta-base")
tokenizer = AutoTokenizer.from_pretrained("almanach/camemberta-base")
CamemBERTa_gen = AutoModelForMaskedLM.from_pretrained("almanach/camemberta-base-generator")
tokenizer_gen = AutoTokenizer.from_pretrained("almanach/camemberta-base-generator")
```
We also include the TF2 weights including the weights for the model's RTD head for the discriminator, and the MLM head for the generator.
CamemBERTa is compatible with most finetuning scripts from the transformers library.
## Pretraining Setup
The model was trained on the French subset of the CCNet corpus (the same subset used in CamemBERT and PaGNOL) and is available on the HuggingFace model hub: CamemBERTa and CamemBERTa Generator.
To speed up the pre-training experiments, the pre-training was split into two phases;
in phase 1, the model is trained with a maximum sequence length of 128 tokens for 10,000 steps with 2,000 warm-up steps and a very large batch size of 67,584.
In phase 2, maximum sequence length is increased to the full model capacity of 512 tokens for 3,300 steps with 200 warm-up steps and a batch size of 27,648.
The model would have seen 133B tokens compared to 419B tokens for CamemBERT-CCNet which was trained for 100K steps, this represents roughly 30% of CamemBERT’s full training.
To have a fair comparison, we trained a RoBERTa model, CamemBERT30%, using the same exact pretraining setup but with the MLM objective.
## Pretraining Loss Curves
check the tensorboard logs and plots
## Fine-tuning results
Datasets: POS tagging and Dependency Parsing (GSD, Rhapsodie, Sequoia, FSMB), NER (FTB), the FLUE benchmark (XNLI, CLS, PAWS-X), and the French Question Answering Dataset (FQuAD)
| Model | UPOS | LAS | NER | CLS | PAWS-X | XNLI | F1 (FQuAD) | EM (FQuAD) |
|-------------------|-----------|-----------|-----------|-----------|-----------|-----------|------------|------------|
| CamemBERT (CCNet) | **97.59** | **88.69** | 89.97 | 94.62 | 91.36 | 81.95 | 80.98 | **62.51** |
| CamemBERT (30%) | 97.53 | 87.98 | **91.04** | 93.28 | 88.94 | 79.89 | 75.14 | 56.19 |
| CamemBERTa | 97.57 | 88.55 | 90.33 | **94.92** | **91.67** | **82.00** | **81.15** | 62.01 |
The following table compares CamemBERTa's performance on XNLI against other models under different training setups, which demonstrates the data efficiency of CamemBERTa.
| Model | XNLI (Acc.) | Training Steps | Tokens seen in pre-training | Dataset Size in Tokens |
|-------------------|-------------|----------------|-----------------------------|------------------------|
| mDeBERTa | 84.4 | 500k | 2T | 2.5T |
| CamemBERTa | 82.0 | 33k | 0.139T | 0.319T |
| XLM-R | 81.4 | 1.5M | 6T | 2.5T |
| CamemBERT - CCNet | 81.95 | 100k | 0.419T | 0.319T |
*Note: The CamemBERTa training steps was adjusted for a batch size of 8192.*
## License
The public model weights are licensed under MIT License.
This code is licensed under the Apache License 2.0.
## Citation
Paper accepted to Findings of ACL 2023.
You can use the preprint citation for now
```
@article{antoun2023camemberta
TITLE = {{Data-Efficient French Language Modeling with CamemBERTa}},
AUTHOR = {Antoun, Wissam and Sagot, Beno{\^i}t and Seddah, Djam{\'e}},
URL = {https://inria.hal.science/hal-03963729},
NOTE = {working paper or preprint},
YEAR = {2023},
MONTH = Jan,
PDF = {https://inria.hal.science/hal-03963729/file/French_DeBERTa___ACL_2023%20to%20be%20uploaded.pdf},
HAL_ID = {hal-03963729},
HAL_VERSION = {v1},
}
```
## Contact
Wissam Antoun: `wissam (dot) antoun (at) inria (dot) fr`
Benoit Sagot: `benoit (dot) sagot (at) inria (dot) fr`
Djame Seddah: `djame (dot) seddah (at) inria (dot) fr` |
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