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Readme.md

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+---
+tags:
+- fp8
+- vllm
+pipeline_tag: text-generation
+base_model: sarvamai/sarvam-105b
+---
+
+# sarvam-105b-FP8-dynamic
+
+## Model Overview
+- **Model Architecture:** sarvamai/sarvam-105b
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight quantization:** FP8
+  - **Activation quantization:** FP8
+- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws).
+- **Version:** 1.0
+- **Model Developers:** RedHatAI
+
+This model is a quantized version of [sarvamai/sarvam-105b](https://huggingface.co/sarvamai/sarvam-105b).
+It was evaluated on several tasks to assess its quality in comparison to the unquantized model.
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights and activations of [sarvamai/sarvam-105b](https://huggingface.co/sarvamai/sarvam-105b) to FP8 data type, ready for inference with vLLM.
+
+Only the weights and activations of the linear operators within transformers blocks are quantized using [LLM Compressor](https://github.com/vllm-project/llm-compressor).
+
+## Deployment
+
+### Use with vLLM
+
+This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend.
+
+
+1. Install vLLM from main:
+```
+uv pip install -U git+https://github.com/vllm-project/vllm.git \
+  --extra-index-url https://wheels.vllm.ai/nightly \
+  --no-deps \
+  --no-cache
+```
+
+2. Run using vLLM
+```python
+from vllm import LLM, SamplingParams
+from transformers import AutoTokenizer
+
+model_id = "RedHatAI/sarvam-105b-FP8-dynamic"
+number_gpus = 1
+
+sampling_params = SamplingParams(temperature=0.6, top_p=0.9, max_tokens=256)
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+messages = [
+    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
+    {"role": "user", "content": "Who are you?"},
+]
+
+prompts = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
+
+llm = LLM(model=model_id, tensor_parallel_size=number_gpus)
+
+outputs = llm.generate(prompts, sampling_params)
+
+generated_text = outputs[0].outputs[0].text
+print(generated_text)
+```
+
+vLLM also supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
+
+## Creation
+
+This model was created by applying [LLM Compressor](https://github.com/vllm-project/llm-compressor), as presented in the code snippet below.
+
+
+<details>
+  <summary>Creation details</summary>
+
+Install specific llm-compression version:
+```
+uv pip install git+https://github.com/vllm-project/llm-compressor.git
+uv pip install --upgrade torchvision --break-system-packages --no-cache
+```
+
+```python
+from compressed_tensors.offload import dispatch_model
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from llmcompressor import oneshot
+from llmcompressor.modifiers.quantization import QuantizationModifier
+
+MODEL_ID = "sarvamai/sarvam-105b"
+
+# Load model.
+model = AutoModelForCausalLM.from_pretrained(MODEL_ID, dtype="auto", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+
+# Configure the quantization algorithm and scheme.
+# In this case, we:
+#   * quantize the weights to fp8 with per channel via ptq
+#   * quantize the activations to fp8 with dynamic per token
+recipe = QuantizationModifier(
+    targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"]
+)
+
+# Apply quantization.
+oneshot(model=model, recipe=recipe)
+
+# Confirm generations of the quantized model look sane.
+print("========== SAMPLE GENERATION ==============")
+dispatch_model(model)
+input_ids = tokenizer("Hello my name is", return_tensors="pt").input_ids.to(
+    model.device
+)
+output = model.generate(input_ids, max_new_tokens=20)
+print(tokenizer.decode(output[0]))
+print("==========================================")
+
+# Save to disk in compressed-tensors format.
+SAVE_DIR = MODEL_ID.rstrip("/").split("/")[-1] + "-FP8-Dynamic"
+model.save_pretrained(SAVE_DIR)
+tokenizer.save_pretrained(SAVE_DIR)
+```
+
+</details>
+
+## Evaluation
+
+This model was evaluated on the well-known text benchmarks using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). 
+
+```
+  lm_eval \
+    --model vllm \
+    --model_args pretrained="RedHatAI/sarvam-105b-FP8-Dynamic",dtype=auto,add_bos_token=True,max_model_len=16384,tensor_parallel_size=2,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \
+    --tasks openllm \
+    --write_out \
+    --batch_size auto \
+    --show_config
+  ```
+
+
+
+### Accuracy
+
+
+| Benchmark | sarvamai/sarvam-105b | RedHatAI/sarvam-105b-FP8-Dynamic | Recovery (%) |
+|---|---|---|---|
+| BBH (exact_match) | 80.86 | 79.93 | 98.84% |
+| GSM8K (strict-match) | 84.38 | 85.37 | 101.17% |
+| GSM8K (flexible-extract) | 84.61 | 85.90 | 101.52% |
+| IFEval (inst_level_strict_acc) | 50.84 | 51.08 | 100.47% |
+| MMLU-Pro (exact_match) | 57.40 | 57.25 | 99.74% |
+| ARC-Challenge (acc) | 65.70 | 66.72 | 101.56% |
+| HellaSwag (acc) | 63.57 | 63.52 | 99.92% |
+| MMLU (acc) | 77.59 | 77.56 | 99.96% |
+| TruthfulQA MC2 (acc) | 51.21 | 51.64 | 100.85% |
+| Winogrande (acc) | 76.32 | 76.40 | 100.10% |