README.md

---
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% |