Instructions to use JamieBradfield/qwen3.8-9b-hermes-function-calling-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JamieBradfield/qwen3.8-9b-hermes-function-calling-v1 with Transformers:

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

pipe = pipeline("text-generation", model="JamieBradfield/qwen3.8-9b-hermes-function-calling-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("JamieBradfield/qwen3.8-9b-hermes-function-calling-v1")
model = AutoModelForCausalLM.from_pretrained("JamieBradfield/qwen3.8-9b-hermes-function-calling-v1", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use JamieBradfield/qwen3.8-9b-hermes-function-calling-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JamieBradfield/qwen3.8-9b-hermes-function-calling-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JamieBradfield/qwen3.8-9b-hermes-function-calling-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/JamieBradfield/qwen3.8-9b-hermes-function-calling-v1

SGLang

How to use JamieBradfield/qwen3.8-9b-hermes-function-calling-v1 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 "JamieBradfield/qwen3.8-9b-hermes-function-calling-v1" \
    --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": "JamieBradfield/qwen3.8-9b-hermes-function-calling-v1",
		"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 "JamieBradfield/qwen3.8-9b-hermes-function-calling-v1" \
        --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": "JamieBradfield/qwen3.8-9b-hermes-function-calling-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use JamieBradfield/qwen3.8-9b-hermes-function-calling-v1 with Docker Model Runner:
```
docker model run hf.co/JamieBradfield/qwen3.8-9b-hermes-function-calling-v1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3.8-9B Hermes Function Calling v1

A QLoRA fine-tune of Empero/Qwen3.8-9B (Qwen3.5 hybrid-architecture distill, 9B dense, 262,144-token context) that specializes the model in Hermes-style tool-call envelopes: given a <tools> XML schema in the system prompt, it emits <tool_call><function=NAME><parameter=KEY>VALUE</parameter></function></tool_call>.

Base model: Empero/Qwen3.8-9B (Apache-2.0)
Training data: 1,090 tool-call conversations rendered in ShareGPT format, all with <tools> XML schemas in the system prompt (synthetic/curated tool-use examples across ~2,000 tool names and dozens of domains)
Method: QLoRA 4-bit, rank 16 / alpha 16, dropout 0, targets q/k/v/o/gate/up/down_proj, lr 2e-4, max seq 4096, batch 2 x grad-accum 4 (effective 8), 2 epochs = 274 steps, cosine schedule
Trainable params: 29,097,984 / 8,980,926,976 (0.32%)
Embeddings: frozen (tool tokens are EOS-anchored copies, not trained)
Vocabulary: resized 248044 → 248079 (2 added tool tokens + base additions)
Weights: BF16 full merge (12 shards), plus a ROCmFPX quantized GGUF (see the GGUF companion repo)

What this model does

The fine-tune teaches the format: when the system prompt carries function signatures inside <tools> </tools> XML tags (the training preamble), the model reliably produces the exact Hermes envelope with correct function names and parameters, with no prose around it.

Format-fidelity probe (45 fresh training-distribution rows, temp 0, identical prompts for both models):

metric	fine-tune	base
fired	43/45 (95.6%)	39/45 (86.7%)
name match	43/45 (95.6%)	39/45 (86.7%)
args OK	43/45 (95.6%)	39/45 (86.7%)
format exact	43/45 (95.6%)	39/45 (86.7%)
emission kind	43 `<tool_call>` envelopes	39 native JSON

The base model answers the same prompts in native Qwen JSON; the fine-tune emits the trained Hermes envelope.

Honest limitations

No trigger generalization. Evaluated on 45 held-out real agent trajectories (ambiguous queries, real tool names like terminal, search_files, patch — none of which appear in training), both the fine-tune and the base score 0/45. A 0.32% LoRA on 1,090 examples does not teach when to decide to call a tool, especially for unseen tool names and ambiguous user intent.
Parity, not gains, in OpenAI-style tool-calling paths. Through an OpenAI-compatible tools parameter (the path most serving stacks use), fine-tune and base perform identically — both fire valid calls at ~55–58 tok/s on this rig; the fine-tune shows no regression and no measurable gain there, because the chat template already formats tool calls server-side and the base was already competent.
The trained envelope only pays off in raw-text tool-call paths: prompts that embed the schema as <tools> XML and parse <tool_call> envelopes from the output (e.g. Hermes's mined-trajectory format, custom parsers).

Reproducibility

All scripts that produced this model are in scripts/:

file	purpose
`qwen38_train.py`	QLoRA training via Unsloth (274 steps, ~19.4 h on an RX 7700 XT 12 GB)
`convert_hf_hermes.py`	ShareGPT training-example converter (the 1,090 rows)
`merge_qwen38.py`	shard-streamed BF16 merge (vision tower dropped; embedding/lm_head resized with EOS-anchor rows; MTP head preserved)
`eval_fc_heldout.py`	Tier-1a eval: held-out real-trajectory trigger test (the 0/45 result)
`probe2_fc.py`	Tier-1b probe: format-fidelity on training-distribution prompts (the 43/45 result)
`subagent-qwen38-workloads.py` + `subagent-qwen38.sh`	Tier-2 harness: Hermes system prompt + real tool schemas, workloads A–E

The scripts carry the author's machine paths (F:/models/..., C:/AI/...) and run on this rig's ROCm stack (Unsloth 2026.8.18, torch 2.11 ROCm, RX 7700 XT); adjust paths for your environment.

Convert/quantize: llama-rocmfpx fork, convert_hf_to_gguf.py --outtype bf16 → llama-quantize Q4_0_ROCMFP4_FAST (see GGUF repo).

Quantized version

Q4_0_ROCMFP4_FAST GGUF (ROCmFPX format, for AMD RDNA3 kernels) is published in the companion repo. Note: ROCmFPX quants target AMD ROCm inference (ROCMFP4 kernels); for portable use, convert from the BF16 merge here.

Acknowledgements

Base model and license inherited from Empero/Qwen3.8-9B (Apache-2.0). Training data derived from Hermes agent trajectories; tool schemas rendered in the Hermes <tools> XML format.

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Safetensors

Model size

9B params

Tensor type

BF16

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