README.md

---
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
---
CURRENTLY IN TRAINING :)

Currently, only the LLM and Classfication section of this model are fully ready.
```py
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
import torch

# Load model and tokenizer
model_name = "HelpingAI/hai3.1-checkpoint-0002"

# Set device to CUDA if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, torch_dtype="auto")
model.to(device)
print(model)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

# Message role format for chat
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": """hlo"""},
]

# Apply chat template to format prompt
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

# Tokenize input and move to device
inputs = tokenizer(prompt, return_tensors="pt")
inputs = {k: v.to(device) for k, v in inputs.items()}

# Set up text streamer for live output
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

# Generate text with streaming
model.generate(
    **inputs,
    max_new_tokens=4089,
    temperature=0.7,
    top_p=0.9,
    do_sample=True,
    streamer=streamer
)
```
Classfication
```py
import os
import json
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Path to saved model (change if needed)
ckpt = "HelpingAI/hai3.1-checkpoint-0002"
device = "cpu"

print("Device:", device)
model = AutoModelForCausalLM.from_pretrained(ckpt, trust_remote_code=True)
model.to(device).eval()

tok = AutoTokenizer.from_pretrained(ckpt, trust_remote_code=True)
if tok.pad_token is None:
    tok.pad_token = tok.eos_token

# Optional: try to load id2label from saved metadata
id2label = None
meta_path = os.path.join(ckpt, "label_map.json")
if os.path.exists(meta_path):
    try:
        with open(meta_path, "r") as f:
            meta = json.load(f)
            id2label = meta.get("id2label")
            print("Loaded id2label from", meta_path)
    except Exception as e:
        print("Failed to read label_map.json:", e)

# Fallback id2label (only used if no metadata)
if id2label is None:
    id2label = ["HARMFUL_SEXUAL","HARMFUL_HATE","HARMFUL_VIOLENCE","HARMFUL_HARASSMENT","HARMFUL_LANGUAGE","HARMFUL_MISINFORMATION","SAFE"]

text = "I am thrilled about my new job!"
enc = tok([text], padding=True, truncation=True, max_length=2048, return_tensors="pt")
enc = {k: v.to(device) for k, v in enc.items()}

with torch.no_grad():
    out = model(input_ids=enc["input_ids"], attention_mask=enc.get("attention_mask"), output_hidden_states=True, return_dict=True, use_cache=False)
    last = out.hidden_states[-1]  # [B, T, H]
    # compute last-token index using attention_mask if available
    if enc.get("attention_mask") is not None:
        idx = (enc["attention_mask"].sum(dim=1) - 1).clamp(min=0)
        pooled = last[torch.arange(last.size(0)), idx]
    else:
        pooled = last[:, -1, :]
    logits = model.structured_lm_head(pooled)
    pred_id = int(logits.argmax(dim=-1).item())

print("Predicted class id:", pred_id)
print("Predicted label:", id2label[pred_id] if pred_id < len(id2label) else "unknown")
```


This model contains layers from our diffrent models
To aline layers we have done post-training after merging layers