tarekziade HF Staff
Publish MIC usage, slim fixture and reviewed Transformers expectations
af86835 verified | # DeepSeek-Coder-V2-Lite-Instruct: causal language modeling with Transformers | |
| This page is the Transformers contract of the DeepSeek-Coder-V2-Lite-Instruct MIC and also a usage example. [doc-builder](https://github.com/huggingface/doc-builder/blob/main/docs/runnable-code-blocks.md) renders the `runnable` blocks below as documentation, and its pytest plugin runs them as written against `deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct`. The MIC runner runs the same blocks against each slim fixture and each checkpoint in [`integration.json`](../../integration.json). Lines marked `# doc-builder: hide` only matter there: they swap in the pinned checkpoint or variant and print the result record. | |
| ## Chat completion | |
| ```py runnable:chat | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed | |
| import json # doc-builder: hide | |
| import os # doc-builder: hide | |
| model_id = "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct" | |
| model_id = os.environ.get("MIC_MODEL", model_id) # doc-builder: hide | |
| variant = json.loads(os.environ.get("MIC_MODEL_KWARGS", "{}")) # doc-builder: hide | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| set_seed(0) # seeds torch, NumPy, and Python: loading, dropout, and data augmentation draw random numbers | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| # The checkpoint stores bfloat16 weights; load in float32 so the training step below is stable. | |
| model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.float32).to(device) | |
| if variant: # doc-builder: hide | |
| del model # doc-builder: hide | |
| model = AutoModelForCausalLM.from_pretrained(model_id, **{"dtype": torch.float32, **variant}).to(device) # doc-builder: hide | |
| # The tokenizer has no pad token; use the EOS token and left padding for batched generation. | |
| if tokenizer.pad_token is None: | |
| tokenizer.pad_token = tokenizer.eos_token | |
| tokenizer.padding_side = "left" | |
| messages_list = [ | |
| [{"role": "user", "content": "Write a Python function that reverses a string."}], | |
| [{"role": "user", "content": "What is the time complexity of binary search?"}], | |
| ] | |
| texts = [tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False) for messages in messages_list] | |
| inputs = tokenizer(texts, return_tensors="pt", padding=True).to(device) | |
| with torch.no_grad(): | |
| logits = model(**inputs).logits | |
| generated = model.generate(**inputs, max_new_tokens=8, do_sample=False) | |
| # Probe the last position of each sequence (left padding, so the last position is the real final token). | |
| probe = logits[:, -1, :5].float().tolist() | |
| generated_text = tokenizer.batch_decode(generated[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True) | |
| print(generated_text) | |
| ``` | |
| ## One training step | |
| ```py runnable:chat:2 | |
| # Language-model labels: copy input_ids and mask padded positions. | |
| labels = inputs["input_ids"].clone() | |
| labels[inputs["attention_mask"] == 0] = -100 | |
| model.train() | |
| optimizer = torch.optim.SGD(model.parameters(), lr=0.01) | |
| loss = model(**inputs, labels=labels).loss | |
| loss.backward() | |
| before = {n: p.detach().cpu().clone() for n, p in model.named_parameters() if p.requires_grad} # doc-builder: hide | |
| optimizer.step() | |
| print(f"loss: {loss.item():.4f}") | |
| if os.environ.get("MIC_RUNNER"): # doc-builder: hide | |
| named = dict(model.named_parameters()) | |
| checked = ("model.layers.0.self_attn.q_proj.weight", "model.layers.0.mlp.gate_proj.weight") | |
| result = { | |
| "runtime": { | |
| "device": model.device.type, | |
| "dtype": str(model.dtype).removeprefix("torch."), | |
| "attn_implementation": model.config._attn_implementation, | |
| }, | |
| "logits": {"probe": probe}, | |
| "generated_text": generated_text, | |
| "training": { | |
| "loss": loss.item(), | |
| "gradients": {n: bool(named[n].grad is not None and torch.count_nonzero(named[n].grad) > 0 and torch.isfinite(named[n].grad).all()) for n in checked}, | |
| "updated": any(not torch.equal(p.detach().cpu(), before[n]) for n, p in model.named_parameters() if n in before), | |
| }, | |
| } | |
| print(json.dumps(result)) | |
| ``` | |