Upload MinjaLM

README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]

config.json

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+{
+  "architectures": [
+    "MinjaLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration.MinjaLMConfig",
+    "AutoModelForCausalLM": "modeling.MinjaLM"
+  },
+  "block_size": 16,
+  "model_type": "minja-lm",
+  "n_embd": 128,
+  "n_head": 2,
+  "n_layer": 2,
+  "torch_dtype": "float32",
+  "transformers_version": "4.52.4",
+  "vocab_size": 32000
+}

configuration.py

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+from transformers import PretrainedConfig
+
+
+class MinjaLMConfig(PretrainedConfig):
+    model_type = "minja-lm"
+
+    def __init__(self, vocab_size=32000, n_embd=128, n_layer=2, n_head=2, block_size=16, **kwargs):
+
+        self.vocab_size = vocab_size
+        self.n_embd = n_embd
+        self.n_layer = n_layer
+        self.n_head = n_head
+        self.block_size = block_size
+        super().__init__(**kwargs)

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:42b623bbbed1c65ed75a4b408c68ac8634c77e8b14e964ac026c45cb118fd13b
+size 37524064

modeling.py

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+import torch
+import torch.nn as nn
+from transformers.modeling_utils import PreTrainedModel
+
+from .configuration import MinjaLMConfig
+
+
+class MinjaLM(PreTrainedModel):
+    """Minimal GPT-style Transformer decoder model."""
+
+    config_class = MinjaLMConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        vocab_size = config.vocab_size
+        n_embd = config.n_embd
+        n_layer = config.n_layer
+        n_head = config.n_head
+        block_size = config.block_size
+
+        self.tok_emb = nn.Embedding(vocab_size, n_embd)  # Token embedding
+        self.pos_emb = nn.Parameter(torch.zeros(1, block_size, n_embd))  # Positional embedding
+        self.drop = nn.Dropout(0.1)
+        self.blocks = nn.ModuleList(
+            [
+                nn.TransformerEncoderLayer(
+                    d_model=n_embd, nhead=n_head, batch_first=True, activation="gelu"
+                )
+                for _ in range(n_layer)
+            ]
+        )
+        self.ln_f = nn.LayerNorm(n_embd)
+        self.head = nn.Linear(n_embd, vocab_size, bias=False)  # Output projection
+
+    def forward(self, idx):
+        # idx: (batch, seq_len)
+        _B, T = idx.size()
+        x = self.tok_emb(idx) + self.pos_emb[:, :T, :]
+        x = self.drop(x)
+        for block in self.blocks:
+            x = block(x)
+        x = self.ln_f(x)
+        logits = self.head(x)
+        return logits
+
+    def generate(self, input_ids, max_new_tokens=20, temperature=0.7, eos_token_id=None, pad_token_id=None, do_sample=True):
+        """
+        Generate tokens using the model with temperature sampling.
+
+        Args:
+            input_ids (torch.Tensor): Input token IDs of shape (batch_size, seq_len)
+            max_new_tokens (int): Maximum number of new tokens to generate
+            temperature (float): Temperature for sampling (higher = more random)
+            eos_token_id (int, optional): Token ID to stop generation
+            pad_token_id (int, optional): Padding token ID (unused for now)
+            do_sample (bool): Whether to use sampling (True) or greedy decoding (False)
+
+        Returns:
+            torch.Tensor: Generated token IDs of shape (batch_size, original_seq_len + generated_tokens)
+        """
+        self.eval()
+        device = input_ids.device
+        self.to(device)
+
+        # Ensure input_ids has the right shape
+        if input_ids.dim() == 1:
+            input_ids = input_ids.unsqueeze(0)
+
+        idx = input_ids.clone()
+
+        with torch.no_grad():
+            for _ in range(max_new_tokens):
+                # Crop to the last block_size tokens if sequence is too long
+                idx_cond = idx[:, -self.config.block_size:] if idx.size(1) > self.config.block_size else idx
+                logits = self(idx_cond)
+                logits = logits[:, -1, :]  # Get the last token's logits
+
+                if do_sample:
+                    logits = logits / temperature
+                    probs = torch.softmax(logits, dim=-1)
+                    next_id = torch.multinomial(probs, num_samples=1)
+                else:
+                    # Greedy decoding
+                    next_id = torch.argmax(logits, dim=-1, keepdim=True)
+
+                idx = torch.cat([idx, next_id], dim=1)
+
+                # Stop if we hit the end-of-sequence token
+                if eos_token_id is not None and next_id.item() == eos_token_id:
+                    break
+
+        return idx