Instructions to use syj4205/broken-model-fixed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use syj4205/broken-model-fixed with Transformers:

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

pipe = pipeline("text-generation", model="syj4205/broken-model-fixed")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("syj4205/broken-model-fixed")
model = AutoModelForCausalLM.from_pretrained("syj4205/broken-model-fixed", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use syj4205/broken-model-fixed with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "syj4205/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": "syj4205/broken-model-fixed",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/syj4205/broken-model-fixed

SGLang

How to use syj4205/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 "syj4205/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": "syj4205/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 "syj4205/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": "syj4205/broken-model-fixed",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use syj4205/broken-model-fixed with Docker Model Runner:
```
docker model run hf.co/syj4205/broken-model-fixed
```

Qwen3-8B Fixed Model

This repository is a fixed version of yunmorning/broken-model.
The original model could not be used to run a functional /chat/completions API server due to two critical issues.

Changes Made

Fix 1: Added `chat_template` to `tokenizer_config.json`

Before: chat_template field did not exist
After: Added official Qwen3 Jinja2 chat template

Why: OpenAI-compatible API servers (vLLM, FriendliAI, etc.) rely on chat_template to convert the messages array into model input via tokenizer.apply_chat_template(). Without this field, the /chat/completions endpoint cannot format prompts and fails entirely.

Fix 2: Corrected shard mapping in `model.safetensors.index.json`

Before: Layer 7's q_proj, k_proj, v_proj pointed to wrong shard
After: Corrected to the right shard

Tensor	Before	After
`model.layers.7.self_attn.q_proj.weight`	`model-00001-of-00005.safetensors`	`model-00002-of-00005.safetensors`
`model.layers.7.self_attn.k_proj.weight`	`model-00001-of-00005.safetensors`	`model-00002-of-00005.safetensors`
`model.layers.7.self_attn.v_proj.weight`	`model-00001-of-00005.safetensors`	`model-00002-of-00005.safetensors`

Why: All other tensors in Layer 7 correctly point to model-00002. This mismatch causes a weight loading error at inference time since the tensors cannot be found in the referenced shard.

Fix 3: Corrected `base_model` metadata in `README.md`

Before: base_model: meta-llama/Meta-Llama-3.1-8B
After: base_model: Qwen/Qwen3-8B

Why: The actual weights (lm_head.weight shape [151936, 4096]) and config (model_type: qwen3, architectures: Qwen3ForCausalLM) confirm this is a Qwen3-8B model. LLaMA 3.1-8B has a vocab size of 128,256, which does not match. This is a metadata-only fix with no effect on inference.

What Was NOT Changed

Item	Reason
`config.json`	All architecture values match Qwen3-8B spec exactly
`tokenizer_class: "Qwen2Tokenizer"`	Qwen3 intentionally reuses Qwen2Tokenizer (same BPE)
`eos_token_id: [151645, 151643]`	Matches official Qwen3 generation config

Verification

from transformers import pipeline
import torch

pipe = pipeline(
    "text-generation",
    model="syj4205/broken-model-fixed",
    dtype=torch.bfloat16,
    device_map="auto",
)

messages = [{"role": "user", "content": "Who are you?"}]
result = pipe(messages, max_new_tokens=40)
print(result[0]["generated_text"][-1]["content"])

Output:

<think> Okay, the user asked, "Who are you?" I need to respond in a friendly and
informative way. Let me start by introducing my name, Qwen...</think>
I'm Qwen, a large language model developed by Alibaba Cloud.

Downloads last month: 14

Safetensors

Model size

8B params

Tensor type

BF16

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