Text Generation
Transformers
TensorBoard
Safetensors
PEFT
llama
Trained with AutoTrain
chain-of-thought
finetuned
conversational
text-generation-inference
Instructions to use devnull37/FalconMind3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devnull37/FalconMind3b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="devnull37/FalconMind3b") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("devnull37/FalconMind3b") model = AutoModelForCausalLM.from_pretrained("devnull37/FalconMind3b", 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=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - PEFT
How to use devnull37/FalconMind3b with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use devnull37/FalconMind3b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "devnull37/FalconMind3b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "devnull37/FalconMind3b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/devnull37/FalconMind3b
- SGLang
How to use devnull37/FalconMind3b 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 "devnull37/FalconMind3b" \ --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": "devnull37/FalconMind3b", "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 "devnull37/FalconMind3b" \ --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": "devnull37/FalconMind3b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use devnull37/FalconMind3b with Docker Model Runner:
docker model run hf.co/devnull37/FalconMind3b
Upload folder using huggingface_hub
Browse files- README.md +46 -0
- config.json +30 -0
- generation_config.json +6 -0
- model-00001-of-00002.safetensors +3 -0
- model-00002-of-00002.safetensors +3 -0
- model.safetensors.index.json +208 -0
- runs/Jan03_11-57-21_4029c56bb8c1/events.out.tfevents.1735905608.4029c56bb8c1.267.0 +2 -2
- special_tokens_map.json +41 -0
- tokenizer.json +0 -0
- tokenizer_config.json +0 -0
- training_args.bin +3 -0
- training_params.json +49 -0
README.md
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---
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tags:
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- autotrain
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- text-generation-inference
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- text-generation
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- peft
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library_name: transformers
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base_model: tiiuae/Falcon3-3B-Instruct
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widget:
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- messages:
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- role: user
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content: What is your favorite condiment?
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license: other
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---
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# Model Trained Using AutoTrain
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This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
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# Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_path = "PATH_TO_THIS_REPO"
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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model = AutoModelForCausalLM.from_pretrained(
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model_path,
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device_map="auto",
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torch_dtype='auto'
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).eval()
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# Prompt content: "hi"
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messages = [
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{"role": "user", "content": "hi"}
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]
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input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
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output_ids = model.generate(input_ids.to('cuda'))
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response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
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# Model response: "Hello! How can I assist you today?"
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print(response)
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```
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config.json
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{
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"_name_or_path": "tiiuae/Falcon3-3B-Instruct",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 11,
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"head_dim": 256,
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"hidden_act": "silu",
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"hidden_size": 3072,
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"initializer_range": 0.02,
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"intermediate_size": 9216,
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"max_position_embeddings": 32768,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 12,
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"num_hidden_layers": 22,
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"num_key_value_heads": 4,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 1000042,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.47.1",
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"use_cache": true,
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"vocab_size": 131072
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 11,
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| 4 |
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"eos_token_id": 11,
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| 5 |
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"transformers_version": "4.47.1"
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}
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model-00001-of-00002.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:f0a06e4dcc55ac6009094dd42b2a1034d67a750f63fbbde312429f5bf6c906fb
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size 4989377856
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model-00002-of-00002.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:bda93cfc164dc808f324afe33f09b5e79000a10424371f8b358efb42038b6bc7
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+
size 1465955576
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model.safetensors.index.json
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{
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| 2 |
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"metadata": {
|
| 3 |
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"total_size": 6455310336
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},
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| 27 |
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"eos_token": {
|
| 28 |
+
"content": "<|endoftext|>",
|
| 29 |
+
"lstrip": false,
|
| 30 |
+
"normalized": false,
|
| 31 |
+
"rstrip": false,
|
| 32 |
+
"single_word": false
|
| 33 |
+
},
|
| 34 |
+
"pad_token": {
|
| 35 |
+
"content": "<|pad|>",
|
| 36 |
+
"lstrip": false,
|
| 37 |
+
"normalized": false,
|
| 38 |
+
"rstrip": false,
|
| 39 |
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"single_word": false
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}
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}
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tokenizer_config.json
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training_args.bin
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| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:46ca3f49d59464099c37223ad5ebd9e256268ab3381cef4d040baf2f19ce51ff
|
| 3 |
+
size 5624
|
training_params.json
ADDED
|
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|
| 1 |
+
{
|
| 2 |
+
"model": "tiiuae/Falcon3-3B-Instruct",
|
| 3 |
+
"project_name": "FinetuneFalcon3b",
|
| 4 |
+
"data_path": "FinetuneFalcon3b/autotrain-data",
|
| 5 |
+
"train_split": "train",
|
| 6 |
+
"valid_split": null,
|
| 7 |
+
"add_eos_token": true,
|
| 8 |
+
"block_size": 1024,
|
| 9 |
+
"model_max_length": 2048,
|
| 10 |
+
"padding": "right",
|
| 11 |
+
"trainer": "sft",
|
| 12 |
+
"use_flash_attention_2": false,
|
| 13 |
+
"log": "tensorboard",
|
| 14 |
+
"disable_gradient_checkpointing": false,
|
| 15 |
+
"logging_steps": -1,
|
| 16 |
+
"eval_strategy": "epoch",
|
| 17 |
+
"save_total_limit": 1,
|
| 18 |
+
"auto_find_batch_size": false,
|
| 19 |
+
"mixed_precision": "fp16",
|
| 20 |
+
"lr": 3e-05,
|
| 21 |
+
"epochs": 15,
|
| 22 |
+
"batch_size": 2,
|
| 23 |
+
"warmup_ratio": 0.1,
|
| 24 |
+
"gradient_accumulation": 4,
|
| 25 |
+
"optimizer": "adamw_torch",
|
| 26 |
+
"scheduler": "linear",
|
| 27 |
+
"weight_decay": 0.0,
|
| 28 |
+
"max_grad_norm": 1.0,
|
| 29 |
+
"seed": 42,
|
| 30 |
+
"chat_template": "none",
|
| 31 |
+
"quantization": "int4",
|
| 32 |
+
"target_modules": "all-linear",
|
| 33 |
+
"merge_adapter": true,
|
| 34 |
+
"peft": true,
|
| 35 |
+
"lora_r": 16,
|
| 36 |
+
"lora_alpha": 32,
|
| 37 |
+
"lora_dropout": 0.1,
|
| 38 |
+
"model_ref": null,
|
| 39 |
+
"dpo_beta": 0.1,
|
| 40 |
+
"max_prompt_length": 128,
|
| 41 |
+
"max_completion_length": null,
|
| 42 |
+
"prompt_text_column": "autotrain_prompt",
|
| 43 |
+
"text_column": "autotrain_text",
|
| 44 |
+
"rejected_text_column": "autotrain_rejected_text",
|
| 45 |
+
"push_to_hub": true,
|
| 46 |
+
"username": "CoolCreator",
|
| 47 |
+
"unsloth": false,
|
| 48 |
+
"distributed_backend": "ddp"
|
| 49 |
+
}
|