Create README.md

README.md

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+---
+license: bigscience-openrail-m
+datasets:
+- nuprl/MultiPL-T
+metrics:
+- code_eval
+library_name: transformers
+tags:
+- code
+- gpt_bigcode
+model-index:
+- name: MultiPLCoder-15b-Lua
+  results:
+  - task:
+      type: text-generation
+    dataset:
+      type: nuprl/MultiPL-E
+      name: MultiPL-HumanEval (Lua)
+    metrics:
+    - name: pass@1
+      type: pass@1
+      value: 0.31
+      verified: true
+- name: MultiPLCoder-15b-Racket
+  results:
+  - task:
+      type: text-generation
+    dataset:
+      type: nuprl/MultiPL-E
+      name: MultiPL-HumanEval (Racket)
+    metrics:
+    - name: pass@1
+      type: pass@1
+      value: 0.21
+      verified: true
+- name: MultiPLCoder-15b-OCaml
+  results:
+  - task:
+      type: text-generation
+    dataset:
+      type: nuprl/MultiPL-E
+      name: MultiPL-HumanEval (OCaml)
+    metrics:
+    - name: pass@1
+      type: pass@1
+      value: 0.199
+      verified: true
+---
+# MultiPLCoder-15b
+
+15 billion parameter version of MultiPLCoder, a set of StarCoder-based models finetuned on the MultiPL-T dataset.
+These models are state-of-the-art at low-resource languages, such as: Lua, Racket, and OCaml.
+
+This 15 billion parameter model is the most capable of the MultiPLCoder family. However, it requires a dedicated GPU for inference.
+For a smaller model that fits on the CPU, check out [MultiPLCoder-1b](https://huggingface.co/nuprl/MultiPLCoder-1b).
+
+
+## Language Revision Index
+
+This is the revision index for the best-performing models for their respective langauge.
+
+| Langauge      | Revision ID | Epoch |
+| ------------- | ----------- | ----- |
+| Lua           | `6069aa54dd554404dd18fccdf5dedd56b8088e74`         | 4    |
+| Racket        | `f0c77c06482f436f469007f20d731cb9dd73d609`         | 8    |
+| OCaml         | `e7babda985786810707200ff885df6105de7dc56`         | 4    |
+
+## Usage
+
+To utilize one of the models in this repository, you must first select a commit revision for that model from the table above.
+For example, to use the Lua model:
+```py
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("nuprl/MultiPLCoder-15b")
+lua_revision="6069aa54dd554404dd18fccdf5dedd56b8088e74"
+model = AutoModelForCausalLM.from_pretrained("nuprl/MultiPLCoder-15b", revision=lua_revision).cuda()
+```
+
+Note that the model's default configuration does not enable caching, therefore you must specify to use the cache on generation.
+```py
+toks = tokenizer.encode("-- Fibonacci iterative", return_tensors="pt")
+out = model.generate(toks, use_cache=True,  do_sample=True, temperature=0.2, top_p=0.95, max_length=50)
+print(tokenizer.decode(out[0], skip_special_tokens=True))
+```
+```
+-- Fibonacci iterative.
+local function fib_iterative(n)
+    if n == 0 or n == 1 then
+        return n
+    end
+    local previous, current = 0, 1
+    for _ = 2, n do
+        previous, current = current, current + previous
+    end
+    return current
+end
+```