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BarraHome/Qwen3.5-27B-Claude-4.6-Opus-FP8-Dynamic
Qwen3.5-27B-Claude-4.6-Opus-FP8-Dynamic is a text generation model from BarraHome. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
This is an FP8 dynamically quantized version of Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled, created using LLM Compressor.
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From the Hugging Face model README
This is an FP8 dynamically quantized version of Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled, created using LLM Compressor.
| 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) |
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:
<think> blocksFor full details on the base model, see the original model card.
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)
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]))
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")