Instructions to use HattoriHanzo1/Qwen-3.8-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HattoriHanzo1/Qwen-3.8-27B with Transformers:

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

pipe = pipeline("text-generation", model="HattoriHanzo1/Qwen-3.8-27B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("HattoriHanzo1/Qwen-3.8-27B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use HattoriHanzo1/Qwen-3.8-27B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HattoriHanzo1/Qwen-3.8-27B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HattoriHanzo1/Qwen-3.8-27B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HattoriHanzo1/Qwen-3.8-27B

SGLang

How to use HattoriHanzo1/Qwen-3.8-27B 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 "HattoriHanzo1/Qwen-3.8-27B" \
    --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": "HattoriHanzo1/Qwen-3.8-27B",
		"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 "HattoriHanzo1/Qwen-3.8-27B" \
        --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": "HattoriHanzo1/Qwen-3.8-27B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use HattoriHanzo1/Qwen-3.8-27B with Docker Model Runner:
```
docker model run hf.co/HattoriHanzo1/Qwen-3.8-27B
```

Qwen3.8-27B — Fixed Chat Template (merged v1.0)

This repository provides a replacement chat_template.jinja for Qwen/Qwen3.8-27B, intended for use with llama.cpp / llama-server (and any GGUF-based backend that accepts a custom chat template).

Why this repo exists

The stock chat template shipped with Qwen3.8-27B crashes with a hard exception when a system / developer message is not the first message in the conversation (e.g. when an agent or client injects system content mid-conversation). With llama-server this surfaces as:

operator(): got exception: {"error":{"code":500,"message":"
------------
While executing CallExpression at line 110, column 28 in source:
...eveloper\" %}
        {{- raise_exception('System message must be at the beginnin...
                                           ^
Error: Jinja Exception: System message must be at the beginning.","type":"server_error"}}

This template eliminates that error. System and developer messages are accepted at any position in the message list and are rendered in place, instead of raising Jinja Exception: System message must be at the beginning.

What the template supports

system / developer messages at any position — no hard exception
Thinking / reasoning control via enable_thinking and reasoning_effort (xhigh (default), medium, low)
preserve_thinking for multi-turn reasoning continuity
Tool calling: function schemas injected in the system block, <tool_call>/<function> rendering, multi-step tool loops
Vision: image (Picture N:) and video (Video N:) placeholders via add_vision_id

Usage

Point your backend at this file:

# llama-server
# or any other GGUF from the Qwen 3.8 27B family — this template fits the whole family and looks very good with all of them
llama-server \
  -m /path/to/Qwen3.8-27B-Q5_K_S.gguf \
  --jinja \
  --chat-template /path/to/chat_template.jinja

Or any other Qwen 3.8 27B GGUF (any quant, any GGUF build of the family) — set the chat template to the contents of chat_template.jinja in any client that allows a custom Jinja chat template (e.g. pi's llamacpp provider, Ollama's TEMPLATE, LM Studio's chat template field).

Files

File	Description
`chat_template.jinja`	The fixed chat template (merged v1.0)

Notes

Weights are not included in this repo — this is a chat-template-only fix for the base model listed above.
If you hit a template error, verify your backend actually supports the Jinja features used (namespace, raise_exception); llama.cpp ≥ 0.16 is recommended.

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for HattoriHanzo1/Qwen-3.8-27B

Base model

Qwen/Qwen3.8-27B

Finetuned

(491)

this model