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

Libraries

How to use Abdulvajid/falcon_sql_model with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Abdulvajid/falcon_sql_model")
model = AutoModelForCausalLM.from_pretrained("Abdulvajid/falcon_sql_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 Abdulvajid/falcon_sql_model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Abdulvajid/falcon_sql_model

SGLang

How to use Abdulvajid/falcon_sql_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 "Abdulvajid/falcon_sql_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": "Abdulvajid/falcon_sql_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 "Abdulvajid/falcon_sql_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": "Abdulvajid/falcon_sql_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use Abdulvajid/falcon_sql_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 Abdulvajid/falcon_sql_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 Abdulvajid/falcon_sql_model to start chatting

Using HuggingFace Spaces for Unsloth

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

Load model with FastModel

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

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

Abdulvajid commited on Oct 31, 2025

Commit

f47fde2

verified ·

1 Parent(s): 1e279c6

(Trained with Unsloth)

Browse files

Files changed (5) hide show

chat_template.jinja +45 -0
config.json +31 -0
special_tokens_map.json +41 -0
tokenizer.json +0 -0
tokenizer_config.json +0 -0

chat_template.jinja ADDED Viewed

	@@ -0,0 +1,45 @@

+{%- if tools %}
+{{- '<|system|>\n' }}
+{%- if messages[0]['role'] == 'system' %}
+{{- messages[0]['content'] }}
+{%- set remaining_messages = messages[1:] %}
+{%- else %}
+{%- set remaining_messages = messages %}
+{%- endif %}
+{{- 'You are a Falcon assistant skilled in function calling. You are helpful, respectful, and concise.\n\n# Tools\n\nYou have access to the following functions. You MUST use them to answer questions when needed. For each function call, you MUST return a JSON object inside <tool_call></tool_call> tags.\n\n<tools>' + tools|tojson(indent=2) + '</tools>\n\n# Output Format\n\nYour response MUST follow this format when making function calls:\n<tool_call>\n[\n  {"name": "function_name", "arguments": {"arg1": "value1", "arg2": "value2"}},\n  {"name": "another_function", "arguments": {"arg": "value"}}\n]\n</tool_call>\nIf no function calls are needed, respond normally without the tool_call tags.\n' }}
+{%- for message in remaining_messages %}
+{%- if message['role'] == 'user' %}
+{{- '<|user|>\n' + message['content'] + '\n' }}
+{%- elif message['role'] == 'assistant' %}
+{%- if message.content %}
+{{- '<|assistant|>\n' + message['content'] }}
+{%- endif %}
+{%- if message.tool_calls %}
+{{- '\n<tool_call>\n' }}
+{{- message.tool_calls|tojson(indent=2) }}
+{{- '\n</tool_call>' }}
+{%- endif %}
+{{- eos_token + '\n' }}
+{%- elif message['role'] == 'tool' %}
+{{- '<|assistant|>\n<tool_response>\n' + message['content'] + '\n</tool_response>\n' }}
+{%- endif %}
+{%- endfor %}
+{{- '<|assistant|>\n' if add_generation_prompt }}
+{%- else %}
+{%- for message in messages %}
+{%- if message['role'] == 'system' %}
+{{- '<|system|>\n' + message['content'] + '\n' }}
+{%- elif message['role'] == 'user' %}
+{{- '<|user|>\n' + message['content'] + '\n' }}
+{%- elif message['role'] == 'assistant' %}
+{%- if not loop.last %}
+{{- '<|assistant|>\n' + message['content'] + eos_token + '\n' }}
+{%- else %}
+{{- '<|assistant|>\n' + message['content'] + eos_token }}
+{%- endif %}
+{%- endif %}
+{%- if loop.last and add_generation_prompt %}
+{{- '<|assistant|>\n' }}
+{%- endif %}
+{%- endfor %}
+{%- endif %}

config.json ADDED Viewed

	@@ -0,0 +1,31 @@

+{
+    "architectures": [
+        "LlamaForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": null,
+    "torch_dtype": "bfloat16",
+    "eos_token_id": 11,
+    "head_dim": 256,
+    "hidden_act": "silu",
+    "hidden_size": 2048,
+    "initializer_range": 0.02,
+    "intermediate_size": 8192,
+    "max_position_embeddings": 8192,
+    "mlp_bias": false,
+    "model_type": "llama",
+    "num_attention_heads": 8,
+    "num_hidden_layers": 18,
+    "num_key_value_heads": 4,
+    "pad_token_id": 2023,
+    "pretraining_tp": 1,
+    "rms_norm_eps": 1e-06,
+    "rope_scaling": null,
+    "rope_theta": 1000042,
+    "tie_word_embeddings": false,
+    "transformers_version": "4.57.1",
+    "unsloth_version": "2025.10.12",
+    "use_cache": false,
+    "vocab_size": 131072
+}

special_tokens_map.json ADDED Viewed

	@@ -0,0 +1,41 @@

+{
+  "additional_special_tokens": [
+    ">>TITLE<<",
+    ">>ABSTRACT<<",
+    ">>INTRODUCTION<<",
+    ">>SUMMARY<<",
+    ">>COMMENT<<",
+    ">>ANSWER<<",
+    ">>QUESTION<<",
+    ">>DOMAIN<<",
+    ">>EMAIL_ADDRESS<<",
+    ">>IP_ADDRESS<<",
+    "<|startoftext|>",
+    ">>IP_ADDRESS_0<<",
+    ">>IP_ADDRESS_1<<",
+    ">>IP_ADDRESS_2<<",
+    ">>IP_ADDRESS_3<<",
+    ">>IP_ADDRESS_4<<",
+    ">>IP_ADDRESS_5<<",
+    ">>IP_ADDRESS_6<<",
+    ">>IP_ADDRESS_7<<",
+    ">>IP_ADDRESS_8<<",
+    ">>IP_ADDRESS_9<<",
+    ">>PASSWORD<<",
+    ">>KEY<<"
+  ],
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|pad|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json ADDED Viewed

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tokenizer_config.json ADDED Viewed

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