Instructions to use joackimagno/Falcon-7B-Recipe-Generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use joackimagno/Falcon-7B-Recipe-Generation with Transformers:

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

pipe = pipeline("text-generation", model="joackimagno/Falcon-7B-Recipe-Generation")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("joackimagno/Falcon-7B-Recipe-Generation")
model = AutoModelForCausalLM.from_pretrained("joackimagno/Falcon-7B-Recipe-Generation")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use joackimagno/Falcon-7B-Recipe-Generation with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "joackimagno/Falcon-7B-Recipe-Generation"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "joackimagno/Falcon-7B-Recipe-Generation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/joackimagno/Falcon-7B-Recipe-Generation

SGLang

How to use joackimagno/Falcon-7B-Recipe-Generation 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 "joackimagno/Falcon-7B-Recipe-Generation" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "joackimagno/Falcon-7B-Recipe-Generation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "joackimagno/Falcon-7B-Recipe-Generation" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "joackimagno/Falcon-7B-Recipe-Generation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio new

How to use joackimagno/Falcon-7B-Recipe-Generation 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 joackimagno/Falcon-7B-Recipe-Generation 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 joackimagno/Falcon-7B-Recipe-Generation to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for joackimagno/Falcon-7B-Recipe-Generation to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="joackimagno/Falcon-7B-Recipe-Generation",
    max_seq_length=2048,
)

Docker Model Runner
How to use joackimagno/Falcon-7B-Recipe-Generation with Docker Model Runner:
```
docker model run hf.co/joackimagno/Falcon-7B-Recipe-Generation
```

joackimagno commited on Jul 7, 2025

Commit

d03b3f5

verified ·

1 Parent(s): 63cbbc9

(Trained with Unsloth)

Browse files

Files changed (5) hide show

config.json +32 -0
generation_config.json +8 -0
special_tokens_map.json +35 -0
tokenizer.json +0 -0
tokenizer_config.json +0 -0

config.json ADDED Viewed

	@@ -0,0 +1,32 @@

+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 11,
+  "head_dim": 256,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 23040,
+  "max_position_embeddings": 32768,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "pad_token_id": 131072,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000042,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.53.0",
+  "unsloth_optimized": true,
+  "unsloth_version": "2025.6.12",
+  "use_cache": true,
+  "vocab_size": 131073
+}

generation_config.json ADDED Viewed

	@@ -0,0 +1,8 @@

+{
+  "_from_model_config": true,
+  "bos_token_id": 11,
+  "eos_token_id": 11,
+  "max_length": 32768,
+  "pad_token_id": 131072,
+  "transformers_version": "4.53.0"
+}

special_tokens_map.json ADDED Viewed

	@@ -0,0 +1,35 @@

+{
+  "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": "<|PAD_TOKEN|>"
+}

tokenizer.json ADDED Viewed

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tokenizer_config.json ADDED Viewed

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