Instructions to use BarraHome/Qwen3.5-27B-Claude-4.6-Opus-FP8-Dynamic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BarraHome/Qwen3.5-27B-Claude-4.6-Opus-FP8-Dynamic with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="BarraHome/Qwen3.5-27B-Claude-4.6-Opus-FP8-Dynamic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BarraHome/Qwen3.5-27B-Claude-4.6-Opus-FP8-Dynamic")
model = AutoModelForCausalLM.from_pretrained("BarraHome/Qwen3.5-27B-Claude-4.6-Opus-FP8-Dynamic", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BarraHome/Qwen3.5-27B-Claude-4.6-Opus-FP8-Dynamic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BarraHome/Qwen3.5-27B-Claude-4.6-Opus-FP8-Dynamic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BarraHome/Qwen3.5-27B-Claude-4.6-Opus-FP8-Dynamic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BarraHome/Qwen3.5-27B-Claude-4.6-Opus-FP8-Dynamic

SGLang

How to use BarraHome/Qwen3.5-27B-Claude-4.6-Opus-FP8-Dynamic with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "BarraHome/Qwen3.5-27B-Claude-4.6-Opus-FP8-Dynamic" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BarraHome/Qwen3.5-27B-Claude-4.6-Opus-FP8-Dynamic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "BarraHome/Qwen3.5-27B-Claude-4.6-Opus-FP8-Dynamic" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BarraHome/Qwen3.5-27B-Claude-4.6-Opus-FP8-Dynamic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use BarraHome/Qwen3.5-27B-Claude-4.6-Opus-FP8-Dynamic with Docker Model Runner:
```
docker model run hf.co/BarraHome/Qwen3.5-27B-Claude-4.6-Opus-FP8-Dynamic
```

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, created using 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.

Usage

With vLLM (recommended)

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

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

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 for the original Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled
Quantization: BarraHome using LLM Compressor

Downloads last month: 10

Safetensors

Model size

27B params

Tensor type

BF16

F8_E4M3

Model tree for BarraHome/Qwen3.5-27B-Claude-4.6-Opus-FP8-Dynamic

Base model

Qwen/Qwen3.5-27B

Finetuned

Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled

Quantized

(38)

this model