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--- |
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license: mit |
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language: |
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- en |
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pipeline_tag: text-generation |
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base_model: |
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- deepseek-ai/DeepSeek-R1-Distill-Qwen-7B |
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tags: |
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- chat |
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library_name: transformers |
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--- |
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# Model Overview |
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- **Model Optimizations:** |
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- **Weight quantization:** FP8 |
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- **Activation quantization:** FP8 |
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- **Release Date:** 1/28/2025 |
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Quantized version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B/) to FP8 data type, ready for inference with SGLang >= 0.3 or vLLM >= 0.5.2. |
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This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. Only the weights and activations of the linear operators within transformers blocks are quantized. |
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## Deployment |
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### Use with SGLang |
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```bash |
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python -m sglang.launch_server --model-path JamAndTeaStudios/DeepSeek-R1-Distill-Qwen-7B-FP8-Dynamic \ |
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--port 30000 --host 0.0.0.0 |
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``` |
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## Creation |
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This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below. |
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<details> |
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<summary>Model Creation Code</summary> |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from llmcompressor.modifiers.quantization import QuantizationModifier |
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from llmcompressor.transformers import oneshot |
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MODEL_ID = "google/gemma-2-27b-it" |
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# 1) Load model. |
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model = AutoModelForCausalLM.from_pretrained( |
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MODEL_ID, device_map="auto", torch_dtype="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) |
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# 2) Configure the quantization algorithm and scheme. |
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# In this case, we: |
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# * quantize the weights to fp8 with per channel via ptq |
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# * quantize the activations to fp8 with dynamic per token |
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recipe = QuantizationModifier( |
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targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"] |
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) |
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# 3) Apply quantization and save in compressed-tensors format. |
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OUTPUT_DIR = MODEL_ID.split("/")[1] + "-FP8-Dynamic" |
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oneshot( |
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model=model, |
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recipe=recipe, |
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tokenizer=tokenizer, |
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output_dir=OUTPUT_DIR, |
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) |
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# Confirm generations of the quantized model look sane. |
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print("========== SAMPLE GENERATION ==============") |
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input_ids = tokenizer("Hello my name is", return_tensors="pt").input_ids.to("cuda") |
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output = model.generate(input_ids, max_new_tokens=20) |
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print(tokenizer.decode(output[0])) |
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print("==========================================") |
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``` |
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</details> |
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## Evaluation |
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TBA |
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## Play Retail Mage |
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 |
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[Retail Mage (Steam)](https://store.steampowered.com/app/3224380/Retail_Mage/) is an immersive sim that uses online LLM inference in almost all features in the gameplay! |
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Reviews |
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“A true to life experience detailing how customer service really works.” |
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10/10 – kpolupo |
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“I enjoyed how many things were flammable in the store.” |
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5/5 – mr_srsbsns |
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“I've only known that talking little crow plushie in MageMart for a day and a half but if anything happened to him I would petrify everyone in this store and then myself.” |
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7/7 – neondenki |
