Update README.md

README.md

@@ -14,11 +14,143 @@ datasets:
 pipeline_tag: text-generation
 ---
 
-# Uploaded  model
+# Llama 3 8B Robot Instruction Model (4-bit)
+
+## Model description
+
+This model is a fine-tuned version of Llama 3 8B, optimized with Unsloth and quantized into 4-bit. 
+It is designed to convert casual user input text into function calls for controlling industrial robots. 
+The aim is to lower the barrier for individuals who do not have programming skills to control robots using simple text instructions. 
+
+## Model Details
+- **Model ID:** Studeni/llama-3-8b-bnb-4bit-robot-instruct
+- **Architecture:** Llama 3 8B
+- **Quantization:** 4-bit
+- **Framework:** Transformers, Peft, Unsloth
+
+## Usage
+
+### Using Unsloth Library
+
+```python
+import json
+from datasets import load_dataset
+from unsloth import FastLanguageModel
+
+# Dataset
+repo_id = "Studeni/robot-instructions"
+dataset = load_dataset(repo_id, split="test")
+test_input = dataset[0]["input"]
+test_output = dataset[0]["output"]
+print(f"User input: {test_input}\nGround truth: {test_output}")
+
+# Prompt
+robot_instruct_prompt = """
+### Instruction:
+Transform input into list of function calls for controlling industrial robots.
+
+### Input:
+{}
+
+### Response:
+{}
+"""
+
+# Model Parameters
+lora_id = "Studeni/llama-3-8b-bnb-4bit-robot-instruct"
+max_seq_length = 2048
+dtype = None  # Auto-detection. Use Float16 for Tesla T4, V100, Bfloat16 for Ampere+
+load_in_4bit = True
+
+# Load the model and tokenizer
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name=lora_id,
+    max_seq_length=max_seq_length,
+    dtype=dtype,
+    load_in_4bit=load_in_4bit,
+)
+FastLanguageModel.for_inference(model)
+
+# Tokenize input text
+inputs = tokenizer(
+    [robot_instruct_prompt.format(test_input, "")],
+    return_tensors="pt",
+).to("cuda")
+
+# Run generation
+outputs = model.generate(**inputs, max_new_tokens=64, use_cache=True)
+text_output = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+
+# Extracting function call and converting to json
+function_call = text_output[0].split("### Response:")[-1].strip()
+function_call = json.loads(function_call)
+for f in function_call:
+    print(f"Function to call: {f['function']}")
+    print(f"Input parameters: {f['kwargs']}")
+```
+
+### Using Transformers and Peft
+```python
+import json
+from datasets import load_dataset
+from peft import AutoPeftModelForCausalLM
+from transformers import AutoTokenizer
+
+# Dataset
+repo_id = "Studeni/robot-instructions"
+dataset = load_dataset(repo_id, split="test")
+test_input = dataset[0]["input"]
+test_output = dataset[0]["output"]
+print(f"User input: {test_input}\nGround truth: {test_output}")
+
+# Prompt
+robot_instruct_prompt = """
+### Instruction:
+Transform input into list of function calls for controlling industrial robots.
+
+### Input:
+{}
+
+### Response:
+{}
+"""
+
+# Model Parameters
+lora_id = "Studeni/llama-3-8b-bnb-4bit-robot-instruct"
+load_in_4bit = True
+
+# Load model and tokenizer
+model = AutoPeftModelForCausalLM.from_pretrained(
+    pretrained_model_name_or_path=lora_id,
+    load_in_4bit=load_in_4bit,
+)
+tokenizer = AutoTokenizer.from_pretrained(lora_id)
+
+# Tokenize input text
+inputs = tokenizer(
+    [robot_instruct_prompt.format(test_input, "")],
+    return_tensors="pt",
+).to("cuda")
+
+# Run generation
+outputs = model.generate(**inputs, max_new_tokens=256, use_cache=True)
+text_output = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+
+# Extracting function call and converting to json
+function_call = text_output[0].split("### Response:")[-1].strip()
+function_call = json.loads(function_call)
+for f in function_call:
+    print(f"Function to call: {f['function']}")
+    print(f"Input parameters: {f['kwargs']}")
+
+```
+
+## Limitations and Future Work 🚨
+This model is currently a work in progress and supports only three basic functions: `move_tcp`, `move_joint`, and `get_joint_values`. 
+Future iterations will include a more comprehensive dataset with more complex commands and capabilities, better human-labeled data, and improved performance metrics.
+
+
 
-- **Developed by:** Studeni
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
 
 This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.