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

Libraries

How to use JunaidSadiq/model with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JunaidSadiq/model")
model = AutoModelForCausalLM.from_pretrained("JunaidSadiq/model")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use JunaidSadiq/model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/JunaidSadiq/model

SGLang

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

Unsloth Studio new

How to use JunaidSadiq/model with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for JunaidSadiq/model to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for JunaidSadiq/model to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for JunaidSadiq/model to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="JunaidSadiq/model",
    max_seq_length=2048,
)

Docker Model Runner
How to use JunaidSadiq/model with Docker Model Runner:
```
docker model run hf.co/JunaidSadiq/model
```

JunaidSadiq commited on Jun 8, 2025

Commit

0dbb9c8

verified ·

1 Parent(s): e7f023a

(Trained with Unsloth)

Browse files

Files changed (4) hide show

config.json +31 -31
generation_config.json +2 -3
tokenizer.json +2 -2
tokenizer_config.json +1 -1

config.json CHANGED Viewed

@@ -1,32 +1,32 @@
 {
-    "architectures": [
-        "Qwen3ForCausalLM"
-    ],
-    "attention_bias": false,
-    "attention_dropout": 0.0,
-    "eos_token_id": 151643,
-    "head_dim": 128,
-    "hidden_act": "silu",
-    "hidden_size": 2560,
-    "initializer_range": 0.02,
-    "intermediate_size": 9728,
-    "max_position_embeddings": 32768,
-    "max_window_layers": 36,
-    "model_type": "qwen3",
-    "num_attention_heads": 32,
-    "num_hidden_layers": 36,
-    "num_key_value_heads": 8,
-    "pad_token_id": 151654,
-    "rms_norm_eps": 1e-06,
-    "rope_scaling": null,
-    "rope_theta": 1000000,
-    "sliding_window": null,
-    "tie_word_embeddings": true,
-    "torch_dtype": "float16",
-    "transformers_version": "4.51.3",
-    "unsloth_fixed": true,
-    "unsloth_version": "2025.6.1",
-    "use_cache": true,
-    "use_sliding_window": false,
-    "vocab_size": 151936
-}

 {
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "eos_token_id": 151643,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151654,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.51.3",
+  "unsloth_fixed": true,
+  "unsloth_version": "2025.6.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json CHANGED Viewed

@@ -1,8 +1,7 @@
 {
-  "bos_token_id": 151643,
   "eos_token_id": 151643,
   "max_length": 32768,
-  "max_new_tokens": 2048,
-  "pad_token_id": 151654,
   "transformers_version": "4.51.3"
 }

 {
+  "_from_model_config": true,
   "eos_token_id": 151643,
   "max_length": 32768,
+  "pad_token_id": 0,
   "transformers_version": "4.51.3"
 }

tokenizer.json CHANGED Viewed

@@ -1,3 +1,3 @@
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-size 11422654

 version https://git-lfs.github.com/spec/v1
+oid sha256:132c0fb88b2070b782a69e8833d01ab987b1198ec606df151512d91820abb758
+size 11422822

tokenizer_config.json CHANGED Viewed

@@ -227,7 +227,7 @@
     "<|video_pad|>"
   ],
   "bos_token": null,
-  "chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|im_start|>user\n' + message['content'] + '<|im_end|>\n' }}{% elif message['role'] == 'system' %}\n{{ '<|im_start|>system\n' + message['im_start|>system\n' + message['content'] + '<|im_end|>\n' }}{% elif message['role'] == 'assistant' %}\n{{ '<|im_start|>assistant\n' + message['content'] + '<|im_end|>\n' }}{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|im_start|>assistant\n' }}{% endif %}\n{% endfor %}",
   "clean_up_tokenization_spaces": false,
   "eos_token": "<|endoftext|>",
   "errors": "replace",

     "<|video_pad|>"
   ],
   "bos_token": null,
+  "chat_template": "{% if messages[0]['role'] == 'system' %}{{ messages[0]['content'] + eos_token }}{% set loop_messages = messages[1:] %}{% else %}{{ 'You are given a problem.\nThink about the problem and provide your working out.\nPlace it between <start_working_out> and <end_working_out>.\nThen, provide your solution between <SOLUTION></SOLUTION>' + eos_token }}{% set loop_messages = messages %}{% endif %}{% for message in loop_messages %}{% if message['role'] == 'user' %}{{ message['content'] }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<start_working_out>' }}{% endif %}",
   "clean_up_tokenization_spaces": false,
   "eos_token": "<|endoftext|>",
   "errors": "replace",