Create README.md

README.md

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+---
+datasets:
+- mzbac/function-calling-phi-3-format-v1.1
+---
+
+# Model
+
+Fine-tuned the Phi3 instruction model for function calling via MLX-LM using https://huggingface.co/datasets/mzbac/function-calling-phi-3-format-v1.1
+
+
+# Usage
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "mzbac/Phi-3-mini-4k-instruct-function-calling"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    attn_implementation="flash_attention_2",
+)
+
+tool = {
+    "name": "search_web",
+    "description": "Perform a web search for a given search terms.",
+    "parameter": {
+        "type": "object",
+        "properties": {
+            "search_terms": {
+                "type": "array",
+                "items": {"type": "string"},
+                "description": "The search queries for which the search is performed.",
+                "required": True,
+            }
+        },
+    },
+}
+
+messages = [
+    {
+        "role": "user",
+        "content": f"You are a helpful assistant with access to the following functions. Use them if required - {str(tool)}",
+    },
+    {"role": "user", "content": "Any news in Melbourne today, May 7, 2024?"},
+]
+
+input_ids = tokenizer.apply_chat_template(
+    messages, add_generation_prompt=True, return_tensors="pt"
+).to(model.device)
+
+terminators = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|end|>")]
+
+outputs = model.generate(
+    input_ids,
+    max_new_tokens=256,
+    eos_token_id=terminators,
+    do_sample=True,
+    temperature=0.1,
+)
+response = outputs[0]
+print(tokenizer.decode(response))
+
+# <s><|user|> You are a helpful assistant with access to the following functions. Use them if required - {'name': 'search_web', 'description': 'Perform a web search for a given search terms.', 'parameter': {'type': 'object', 'properties': {'search_terms': {'type': 'array', 'items': {'type': 'string'}, 'description': 'The search queries for which the search is performed.', 'required': True}}}}<|end|><|assistant|>
+# <|user|> Any news in Melbourne today, May 7, 2024?<|end|>
+# <|assistant|> <functioncall> {"name": "search_web", "arguments": {"search_terms": ["news", "Melbourne", "May 7, 2024"]}}<|end|>
+```
+
+# Training hyperparameters
+lora_config.yaml
+```yaml
+# The path to the local model directory or Hugging Face repo.
+model: "microsoft/Phi-3-mini-4k-instruct"
+# Whether or not to train (boolean)
+train: true
+
+# Directory with {train, valid, test}.jsonl files
+data: "data"
+
+# The PRNG seed
+seed: 0
+
+# Number of layers to fine-tune
+lora_layers: 32
+
+# Minibatch size.
+batch_size: 1
+
+# Iterations to train for.
+iters: 111000
+
+# Number of validation batches, -1 uses the entire validation set.
+val_batches: -1
+
+# Adam learning rate.
+learning_rate: 1e-6
+
+# Number of training steps between loss reporting.
+steps_per_report: 10
+
+# Number of training steps between validations.
+steps_per_eval: 200
+
+# Load path to resume training with the given adapter weights.
+# resume_adapter_file: "adapters/adapters.safetensors"
+
+# Save/load path for the trained adapter weights.
+adapter_path: "adapters"
+
+# Save the model every N iterations.
+save_every: 1000
+
+# Evaluate on the test set after training
+test: false
+
+# Number of test set batches, -1 uses the entire test set.
+test_batches: 100
+
+# Maximum sequence length.
+max_seq_length: 4096
+
+# Use gradient checkpointing to reduce memory use.
+grad_checkpoint: false
+
+# LoRA parameters can only be specified in a config file
+lora_parameters:
+  # The layer keys to apply LoRA to.
+  # These will be applied for the last lora_layers
+  keys: ['mlp.down_proj','mlp.gate_up_proj','self_attn.qkv_proj','self_attn.o_proj']
+  rank: 128
+  alpha: 256
+  scale: 10.0
+  dropout: 0.05
+```