---
base_model: Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled
library_name: transformers
tags:
  - fp8
  - quantized
  - compressed-tensors
  - llmcompressor
  - qwen3.5
  - vllm
license: apache-2.0
---

# Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled — FP8 Dynamic

This is an **FP8 dynamically quantized** version of [Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled](https://huggingface.co/Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled), created using [LLM Compressor](https://github.com/vllm-project/llm-compressor).

## Quantization Details

| | Details |
|---|---|
| **Method** | W8A8 FP8 Dynamic |
| **Weights** | FP8 (E4M3), per-channel, symmetric |
| **Activations** | FP8 (E4M3), dynamic per-token, symmetric |
| **Ignored layers** | `lm_head` |
| **Format** | `compressed-tensors` |
| **Calibration data** | None required (PTQ) |

## About the Base Model

Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled is a reasoning-focused model fine-tuned from Qwen3.5-27B using Chain-of-Thought (CoT) distillation from Claude 4.6 Opus. It uses `<think>` tags for structured internal reasoning before providing answers.

**Key capabilities:**
- Modular & structured thinking in `<think>` blocks
- Tool calling and coding agent support (Claude Code, OpenCode)
- 262K token context length
- Native developer role support

For full details on the base model, see the [original model card](https://huggingface.co/Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled).

## Usage

### With vLLM (recommended)

```python
from vllm import LLM, SamplingParams

model = LLM(model="BarraHome/Qwen3.5-27B-Claude-4.6-Opus-FP8-Dynamic")
sampling_params = SamplingParams(max_tokens=2048, temperature=0.6)
output = model.generate(["Hello, tell me about yourself"], sampling_params)
print(output[0].outputs[0].text)
```

### With Transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "BarraHome/Qwen3.5-27B-Claude-4.6-Opus-FP8-Dynamic",
    device_map="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
    "BarraHome/Qwen3.5-27B-Claude-4.6-Opus-FP8-Dynamic",
    trust_remote_code=True,
)

input_ids = tokenizer("Hello my name is", return_tensors="pt").input_ids.to(model.device)
output = model.generate(input_ids, max_new_tokens=100)
print(tokenizer.decode(output[0]))
```

## How It Was Made

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from llmcompressor import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

model = AutoModelForCausalLM.from_pretrained(
    "Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled",
    dtype="auto",
    low_cpu_mem_usage=True,
    trust_remote_code=True,
)

recipe = QuantizationModifier(
    targets="Linear",
    scheme="FP8_DYNAMIC",
    ignore=["lm_head"],
)

oneshot(model=model, recipe=recipe, output_dir="Qwen3.5-27B-Claude-4.6-Opus-FP8-Dynamic")
```

## Credits

- **Base model:** [Jackrong](https://huggingface.co/Jackrong) for the original Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled
- **Quantization:** [BarraHome](https://huggingface.co/BarraHome) using [LLM Compressor](https://github.com/vllm-project/llm-compressor)