Instructions to use majimenez/broken-model-fixed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use majimenez/broken-model-fixed with Transformers:

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

pipe = pipeline("text-generation", model="majimenez/broken-model-fixed")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("majimenez/broken-model-fixed")
model = AutoModelForCausalLM.from_pretrained("majimenez/broken-model-fixed")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use majimenez/broken-model-fixed with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/majimenez/broken-model-fixed

SGLang

How to use majimenez/broken-model-fixed 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 "majimenez/broken-model-fixed" \
    --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": "majimenez/broken-model-fixed",
		"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 "majimenez/broken-model-fixed" \
        --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": "majimenez/broken-model-fixed",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use majimenez/broken-model-fixed with Docker Model Runner:
```
docker model run hf.co/majimenez/broken-model-fixed
```

broken-model-fixed

This repository is a fixed copy of yunmorning/broken-model, which was reported as unable to serve a functional /chat/completions API endpoint.

Root Cause

The model weights, architecture config, and tokenizer vocabulary are all valid Qwen3-8B artifacts. However, the tokenizer_config.json was missing the chat_template field.

The chat_template is a Jinja2 template that tells inference servers (vLLM, TGI, FriendliAI, etc.) how to convert a list of chat messages ([{"role": "user", "content": "Hello"}]) into the model's expected prompt format (ChatML with <|im_start|>/<|im_end|> delimiters). Without it, any /chat/completions request fails with:

ValueError: Cannot use chat template functions because tokenizer.chat_template is not set

Changes Made

1. `tokenizer_config.json` — added `chat_template` (critical fix)

Added the standard Qwen3-8B chat template (4168 characters) from the reference model Qwen/Qwen3-8B. This was the only field missing; all other tokenizer config values were already correct and identical to the reference.

The template handles:

System, user, and assistant message formatting using ChatML (<|im_start|>/<|im_end|>)
Tool/function calling via <tool_call> and <tool_response> tags
Reasoning/thinking blocks via <think> tags
Generation prompt injection for inference

2. `README.md` — corrected `base_model` metadata

The original README declared base_model: meta-llama/Llama-3.1-8B, but the model is actually Qwen3-8B based on:

config.json: architectures: ["Qwen3ForCausalLM"], model_type: "qwen3"
Weight tensors contain Qwen3-specific layers (q_norm, k_norm)
tokenizer_config.json: tokenizer_class: "Qwen2Tokenizer", vocab_size 151936
36 hidden layers and intermediate_size 12288 (matching Qwen3-8B, not Llama-3.1-8B which has 32 layers and intermediate_size 14336)

Verification on FriendliAI

When importing the broken model (yunmorning/broken-model) into FriendliAI, both "Tool call" and "Reasoning parser" features were detected as "Not supported", and endpoint creation failed with an internal system error.

After applying the fix, importing majimenez/broken-model-fixed into FriendliAI correctly detects both features as "Supported". This is because FriendliAI parses the chat_template to detect these capabilities:

Tool call: the template contains <tool_call>/</tool_call> and <tool_response>/</tool_response> formatting logic
Reasoning parser: the template contains <think>/</think> block handling with enable_thinking support

Without a chat_template at all, neither feature can be detected or used.

End-to-end confirmation

Broken model (yunmorning/broken-model): Failed to load entirely on FriendliAI — endpoint creation returned an internal system error.
Fixed model (majimenez/broken-model-fixed): Deployed successfully on FriendliAI and responded correctly to /chat/completions requests, producing both reasoning (<think> blocks) and body response text.

Files NOT changed

All other files were verified correct and left unmodified:

config.json — identical to reference Qwen/Qwen3-8B
generation_config.json — identical to reference Qwen/Qwen3-8B
tokenizer.json, vocab.json, merges.txt — tokenizer vocabulary files
model-*.safetensors / model.safetensors.index.json — model weights

Downloads last month: 8

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for majimenez/broken-model-fixed

Base model

Qwen/Qwen3-8B-Base

Finetuned

Qwen/Qwen3-8B