--- tags: - fp8 - vllm pipeline_tag: text-generation provider: Sarvam AI license: apache-2.0 license_name: apache-2.0 name: RedHatAI/sarvam-30b-FP8-dynamic license_link: https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md readme: https://huggingface.co/RedHatAI/sarvam-30b-FP8-dynamic/blob/main/README.md description: Sarvam-30B is an advanced Mixture-of-Experts (MoE) model with 2.4B non-embedding active parameters, designed primarily for practical deployment. It combines strong reasoning, reliable coding ability, and best-in-class conversational quality across Indian languages. Sarvam-30B is built to run reliably in resource-constrained environments and can handle multilingual voice calls while performing tool calls. base_model: - sarvamai/sarvam-30b validated_on: - RHOAI 3.4 - RHAIIS 3.4 language: - en - hi - bn - ta - te - mr - gu - kn - ml - pa - or - as - ur - sa - ne - sd - kok - mai - doi - mni - sat - ks - bo ---

sarvam-30b-FP8-dynamic Model Icon

Validated Badge ## Model Overview - **Model Architecture:** sarvamai/sarvam-30b - **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 - **ModelCar Storage URI:** oci://registry.redhat.io/rhai/modelcar-sarvam-30b-fp8-dynamic:3.0 This model is a quantized version of [sarvamai/sarvam-30b](https://huggingface.co/sarvamai/sarvam-30b). 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-30b](https://huggingface.co/sarvamai/sarvam-30b) 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-30b-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.
Creation details 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-30b" # 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) ```
## 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-30b-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-30b | RedHatAI/sarvam-30b-FP8-Dynamic | Recovery (%) | |---|---|---|---| | BBH (exact_match) | 63.32 | 62.95 | 99.42% | | GSM8K (strict-match) | 72.33 | 72.40 | 100.10% | | GSM8K (flexible-extract) | 69.67 | 70.81 | 101.63% | | IFEval (inst_level_strict_acc) | 34.17 | 31.65 | 92.63% | | MMLU-Pro (exact_match) | 45.69 | 45.81 | 100.25% | | ARC-Challenge (acc) | 58.28 | 57.76 | 99.12% | | HellaSwag (acc) | 53.98 | 53.98 | 100.00% | | MMLU (acc) | 66.20 | 66.15 | 99.92% | | TruthfulQA MC2 (acc) | 50.34 | 50.58 | 100.48% | | Winogrande (acc) | 61.09 | 61.17 | 100.13% |