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

Libraries

How to use katiyardinesh/DKatiyar-fixed with Transformers:

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

pipe = pipeline("text-generation", model="katiyardinesh/DKatiyar-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("katiyardinesh/DKatiyar-fixed")
model = AutoModelForCausalLM.from_pretrained("katiyardinesh/DKatiyar-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 katiyardinesh/DKatiyar-fixed with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/katiyardinesh/DKatiyar-fixed

SGLang

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

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

DKatiyar-fixed

Fixed version of yunmorning/broken-model.

Changes Made

1. Added missing `chat_template` to `tokenizer_config.json`

The original repo had no chat_template field. Without it, the serving engine cannot format role-based messages (system, user, assistant) with the special tokens the model expects (<|im_start|>, <|im_end|>). Raw text is sent instead, producing garbled output because the model was trained on the ChatML format.

The fix: copied the chat_template from the official Qwen/Qwen3-8B repository. This template handles system/user/assistant role formatting, tool calls (<tool_call>/</tool_call>), thinking mode (<think>/</think>), and the enable_thinking toggle.

2. Corrected `base_model` in README metadata

The original README listed meta-llama/Meta-Llama-3.1-8B as the base model. The actual architecture is Qwen3: config.json specifies Qwen3ForCausalLM with 36 layers, 4096 hidden_size, 32 attention heads, 8 KV heads, and vocab_size 151936. The tokenizer is Qwen2Tokenizer. These all match Qwen/Qwen3-8B, not Meta-Llama.

Downloads last month: 7

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for katiyardinesh/DKatiyar-fixed

Base model

Qwen/Qwen3-8B-Base

Finetuned

Qwen/Qwen3-8B

Finetuned

(2259)

this model

DKatiyar-fixed

Changes Made

1. Added missing chat_template to tokenizer_config.json

2. Corrected base_model in README metadata

Model tree for katiyardinesh/DKatiyar-fixed

1. Added missing `chat_template` to `tokenizer_config.json`

2. Corrected `base_model` in README metadata