File size: 2,520 Bytes
7bdd781
 
 
 
 
76610df
7bdd781
76610df
7bdd781
 
 
76610df
 
 
 
 
 
7bdd781
76610df
7bdd781
76610df
7bdd781
76610df
7bdd781
76610df
 
 
 
 
 
7bdd781
 
 
 
 
76610df
 
 
 
7bdd781
76610df
7bdd781
76610df
7bdd781
 
 
76610df
 
 
 
7bdd781
76610df
7bdd781
76610df
 
 
7bdd781
76610df
 
 
7bdd781
76610df
 
7bdd781
76610df
 
 
 
 
 
7bdd781
 
 
 
 
76610df
7bdd781
 
 
76610df
 
 
7bdd781
 
 
76610df
 
 
 
7bdd781
76610df
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
---
base_model: allenai/OLMo-1B-hf
library_name: peft
---

# OLMo Code Python3 Text-Only Model

This is a LoRA adapter fine-tuned on the OLMo-1B model for Python 3 code generation tasks.

## Model Details

- **Base Model:** allenai/OLMo-1B-hf
- **Model Type:** LoRA Adapter
- **Task:** Causal Language Modeling for Python 3 code
- **Language:** Python 3
- **License:** MIT
- **Fine-tuned by:** dipikakhullar

## Model Description

This model is a LoRA adapter that has been fine-tuned on Python 3 code data. It extends the capabilities of the base OLMo-1B model specifically for Python code generation tasks.

### LoRA Configuration

- **LoRA Type:** LORA
- **LoRA Alpha:** 16
- **LoRA Dropout:** 0.05
- **LoRA Rank (r):** 8
- **Target Modules:** down_proj, q_proj, v_proj, up_proj, k_proj, gate_proj, o_proj
- **Task Type:** CAUSAL_LM

## Uses

### Direct Use

This model is intended for Python 3 code generation tasks. It can be used to:
- Generate Python code completions
- Assist with code writing
- Provide code suggestions

### Downstream Use

The model can be further fine-tuned for specific Python programming tasks or integrated into code generation applications.

### Out-of-Scope Use

This model is specifically designed for Python 3 code generation and may not perform well for:
- Other programming languages
- Natural language tasks
- Non-code related tasks

## How to Get Started with the Model

```python
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the base model and tokenizer
base_model = AutoModelForCausalLM.from_pretrained("allenai/OLMo-1B-hf")
tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-1B-hf")

# Load the LoRA adapter
model = PeftModel.from_pretrained(base_model, "dipikakhullar/olmo-code-python3-text-only")

# Example usage
prompt = "def fibonacci(n):"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Training Details

### Training Data

The model was fine-tuned on cleaned Python 3 code data specifically prepared for language model training.

### Training Procedure

- **Base Model:** allenai/OLMo-1B-hf
- **Fine-tuning Method:** LoRA (Low-Rank Adaptation)
- **Checkpoint:** checkpoint-6000

## Model Card Contact

- **Author:** dipikakhullar
- **Repository:** https://huggingface.co/dipikakhullar/olmo-code-python3-text-only

## Framework versions

- PEFT 0.7.1
- Transformers