Black formatting

examples/fastapi_server.py

@@ -5,9 +5,11 @@ from llama_cpp import Llama
 from fastapi import FastAPI
 from pydantic import BaseModel, BaseSettings, Field
 
+
 class Settings(BaseSettings):
     model: str
 
+
 app = FastAPI(
     title="🦙 llama.cpp Python API",
     version="0.0.1",
@@ -15,6 +17,7 @@ app = FastAPI(
 settings = Settings()
 llama = Llama(settings.model)
 
+
 class CompletionRequest(BaseModel):
     prompt: str
     suffix: Optional[str] = Field(None)
@@ -31,12 +34,11 @@ class CompletionRequest(BaseModel):
         schema_extra = {
             "example": {
                 "prompt": "\n\n### Instructions:\nWhat is the capital of France?\n\n### Response:\n",
-                "stop": ["\n", "###"]
+                "stop": ["\n", "###"],
             }
         }
 
 
-
 @app.post("/v1/completions")
 def completions(request: CompletionRequest):
-    return llama(**request.dict())
+    return llama(**request.dict())

examples/high_level_api_basic_inference.py

@@ -9,6 +9,11 @@ args = parser.parse_args()
 
 llm = Llama(model_path=args.model)
 
-output = llm("Question: What are the names of the planets in the solar system? Answer: ", max_tokens=48, stop=["Q:", "\n"], echo=True)
+output = llm(
+    "Question: What are the names of the planets in the solar system? Answer: ",
+    max_tokens=48,
+    stop=["Q:", "\n"],
+    echo=True,
+)
 
-print(json.dumps(output, indent=2))
+print(json.dumps(output, indent=2))

examples/langchain_custom_llm.py

@@ -5,6 +5,7 @@ from llama_cpp import Llama
 from langchain.llms.base import LLM
 from typing import Optional, List, Mapping, Any
 
+
 class LlamaLLM(LLM):
     model_path: str
     llm: Llama
@@ -16,7 +17,7 @@ class LlamaLLM(LLM):
     def __init__(self, model_path: str, **kwargs: Any):
         model_path = model_path
         llm = Llama(model_path=model_path)
-        super().__init__(model_path=model_path, llm=llm,  **kwargs)
+        super().__init__(model_path=model_path, llm=llm, **kwargs)
 
     def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
         response = self.llm(prompt, stop=stop or [])
@@ -26,6 +27,7 @@ class LlamaLLM(LLM):
     def _identifying_params(self) -> Mapping[str, Any]:
         return {"model_path": self.model_path}
 
+
 parser = argparse.ArgumentParser()
 parser.add_argument("-m", "--model", type=str, default="./models/...")
 args = parser.parse_args()
@@ -34,7 +36,9 @@ args = parser.parse_args()
 llm = LlamaLLM(model_path=args.model)
 
 # Basic Q&A
-answer = llm("Question: What is the capital of France? Answer: ", stop=["Question:", "\n"])
+answer = llm(
+    "Question: What is the capital of France? Answer: ", stop=["Question:", "\n"]
+)
 print(f"Answer: {answer.strip()}")
 
 # Using in a chain
@@ -48,4 +52,4 @@ prompt = PromptTemplate(
 chain = LLMChain(llm=llm, prompt=prompt)
 
 # Run the chain only specifying the input variable.
-print(chain.run("colorful socks"))
+print(chain.run("colorful socks"))

examples/low_level_api_inference.py

@@ -27,7 +27,15 @@ embd = embd_inp
 n = 8
 
 for i in range(n):
-    id = llama_cpp.llama_sample_top_p_top_k(ctx, (llama_cpp.c_int * len(embd))(*embd), n_of_tok + i, 40, 0.8, 0.2, 1.0/0.85)
+    id = llama_cpp.llama_sample_top_p_top_k(
+        ctx,
+        (llama_cpp.c_int * len(embd))(*embd),
+        n_of_tok + i,
+        40,
+        0.8,
+        0.2,
+        1.0 / 0.85,
+    )
 
     embd.append(id)
 
@@ -38,4 +46,4 @@ for i in range(n):
 
 llama_cpp.llama_free(ctx)
 
-print(prediction.decode("utf-8"))
+print(prediction.decode("utf-8"))

llama_cpp/llama.py

@@ -5,6 +5,7 @@ from typing import List, Optional
 
 from . import llama_cpp
 
+
 class Llama:
     def __init__(
         self,
@@ -82,7 +83,10 @@ class Llama:
 
         for i in range(max_tokens):
             tokens_seen = prompt_tokens + completion_tokens
-            last_n_tokens = [0] * max(0, self.last_n - tokens_seen)  + [self.tokens[j] for j in range(max(tokens_seen - self.last_n, 0), tokens_seen)]
+            last_n_tokens = [0] * max(0, self.last_n - tokens_seen) + [
+                self.tokens[j]
+                for j in range(max(tokens_seen - self.last_n, 0), tokens_seen)
+            ]
 
             token = llama_cpp.llama_sample_top_p_top_k(
                 self.ctx,
@@ -128,9 +132,8 @@ class Llama:
                 self.ctx,
             )[:logprobs]
 
-
         return {
-            "id": f"cmpl-{str(uuid.uuid4())}", # Likely to change
+            "id": f"cmpl-{str(uuid.uuid4())}",  # Likely to change
             "object": "text_completion",
             "created": int(time.time()),
             "model": self.model_path,
@@ -151,5 +154,3 @@ class Llama:
 
     def __del__(self):
         llama_cpp.llama_free(self.ctx)
-
-

llama_cpp/llama_cpp.py

@@ -1,6 +1,15 @@
 import ctypes
 
-from ctypes import c_int, c_float, c_double, c_char_p, c_void_p, c_bool, POINTER, Structure
+from ctypes import (
+    c_int,
+    c_float,
+    c_double,
+    c_char_p,
+    c_void_p,
+    c_bool,
+    POINTER,
+    Structure,
+)
 
 import pathlib
 
@@ -13,26 +22,32 @@ lib = ctypes.CDLL(str(libfile))
 llama_token = c_int
 llama_token_p = POINTER(llama_token)
 
+
 class llama_token_data(Structure):
     _fields_ = [
-        ('id', llama_token), # token id
+        ("id", llama_token),  # token id
-        ('p', c_float), # probability of the token
+        ("p", c_float),  # probability of the token
-        ('plog', c_float), # log probability of the token
+        ("plog", c_float),  # log probability of the token
     ]
 
+
 llama_token_data_p = POINTER(llama_token_data)
 
+
 class llama_context_params(Structure):
     _fields_ = [
-        ('n_ctx', c_int), # text context
+        ("n_ctx", c_int),  # text context
-        ('n_parts', c_int), # -1 for default
+        ("n_parts", c_int),  # -1 for default
-        ('seed', c_int), # RNG seed, 0 for random
+        ("seed", c_int),  # RNG seed, 0 for random
-        ('f16_kv', c_bool), # use fp16 for KV cache
+        ("f16_kv", c_bool),  # use fp16 for KV cache
-        ('logits_all', c_bool), # the llama_eval() call computes all logits, not just the last one
+        (
-
+            "logits_all",
-        ('vocab_only', c_bool), # only load the vocabulary, no weights
+            c_bool,
+        ),  # the llama_eval() call computes all logits, not just the last one
+        ("vocab_only", c_bool),  # only load the vocabulary, no weights
     ]
 
+
 llama_context_params_p = POINTER(llama_context_params)
 
 llama_context_p = c_void_p
@@ -74,7 +89,15 @@ lib.llama_token_bos.restype = llama_token
 lib.llama_token_eos.argtypes = []
 lib.llama_token_eos.restype = llama_token
 
-lib.llama_sample_top_p_top_k.argtypes = [llama_context_p, llama_token_p, c_int, c_int, c_double, c_double, c_double]
+lib.llama_sample_top_p_top_k.argtypes = [
+    llama_context_p,
+    llama_token_p,
+    c_int,
+    c_int,
+    c_double,
+    c_double,
+    c_double,
+]
 lib.llama_sample_top_p_top_k.restype = llama_token
 
 lib.llama_print_timings.argtypes = [llama_context_p]
@@ -86,45 +109,71 @@ lib.llama_reset_timings.restype = None
 lib.llama_print_system_info.argtypes = []
 lib.llama_print_system_info.restype = c_char_p
 
+
 # Python functions
 def llama_context_default_params() -> llama_context_params:
     params = lib.llama_context_default_params()
     return params
 
-def llama_init_from_file(path_model: bytes, params: llama_context_params) -> llama_context_p:
+
+def llama_init_from_file(
+    path_model: bytes, params: llama_context_params
+) -> llama_context_p:
     """Various functions for loading a ggml llama model.
     Allocate (almost) all memory needed for the model.
-    Return NULL on failure """
+    Return NULL on failure"""
     return lib.llama_init_from_file(path_model, params)
 
+
 def llama_free(ctx: llama_context_p):
     """Free all allocated memory"""
     lib.llama_free(ctx)
 
-def llama_model_quantize(fname_inp: bytes, fname_out: bytes, itype: c_int, qk: c_int) -> c_int:
+
+def llama_model_quantize(
+    fname_inp: bytes, fname_out: bytes, itype: c_int, qk: c_int
+) -> c_int:
     """Returns 0 on success"""
     return lib.llama_model_quantize(fname_inp, fname_out, itype, qk)
 
-def llama_eval(ctx: llama_context_p, tokens: llama_token_p, n_tokens: c_int, n_past: c_int, n_threads: c_int) -> c_int:
+
+def llama_eval(
+    ctx: llama_context_p,
+    tokens: llama_token_p,
+    n_tokens: c_int,
+    n_past: c_int,
+    n_threads: c_int,
+) -> c_int:
     """Run the llama inference to obtain the logits and probabilities for the next token.
     tokens + n_tokens is the provided batch of new tokens to process
     n_past is the number of tokens to use from previous eval calls
     Returns 0 on success"""
     return lib.llama_eval(ctx, tokens, n_tokens, n_past, n_threads)
 
-def llama_tokenize(ctx: llama_context_p, text: bytes, tokens: llama_token_p, n_max_tokens: c_int, add_bos: c_bool) -> c_int:
+
+def llama_tokenize(
+    ctx: llama_context_p,
+    text: bytes,
+    tokens: llama_token_p,
+    n_max_tokens: c_int,
+    add_bos: c_bool,
+) -> c_int:
     """Convert the provided text into tokens.
     The tokens pointer must be large enough to hold the resulting tokens.
     Returns the number of tokens on success, no more than n_max_tokens
-    Returns a negative number on failure - the number of tokens that would have been returned"""
+    Returns a negative number on failure - the number of tokens that would have been returned
+    """
     return lib.llama_tokenize(ctx, text, tokens, n_max_tokens, add_bos)
 
+
 def llama_n_vocab(ctx: llama_context_p) -> c_int:
     return lib.llama_n_vocab(ctx)
 
+
 def llama_n_ctx(ctx: llama_context_p) -> c_int:
     return lib.llama_n_ctx(ctx)
 
+
 def llama_get_logits(ctx: llama_context_p):
     """Token logits obtained from the last call to llama_eval()
     The logits for the last token are stored in the last row
@@ -133,25 +182,42 @@ def llama_get_logits(ctx: llama_context_p):
     Cols: n_vocab"""
     return lib.llama_get_logits(ctx)
 
+
 def llama_token_to_str(ctx: llama_context_p, token: int) -> bytes:
     """Token Id -> String. Uses the vocabulary in the provided context"""
     return lib.llama_token_to_str(ctx, token)
 
+
 def llama_token_bos() -> llama_token:
     return lib.llama_token_bos()
 
+
 def llama_token_eos() -> llama_token:
     return lib.llama_token_eos()
 
-def llama_sample_top_p_top_k(ctx: llama_context_p, last_n_tokens_data: llama_token_p, last_n_tokens_size: c_int, top_k: c_int, top_p: c_double, temp: c_double, repeat_penalty: c_double) -> llama_token:
+
-    return lib.llama_sample_top_p_top_k(ctx, last_n_tokens_data, last_n_tokens_size, top_k, top_p, temp, repeat_penalty)
+def llama_sample_top_p_top_k(
+    ctx: llama_context_p,
+    last_n_tokens_data: llama_token_p,
+    last_n_tokens_size: c_int,
+    top_k: c_int,
+    top_p: c_double,
+    temp: c_double,
+    repeat_penalty: c_double,
+) -> llama_token:
+    return lib.llama_sample_top_p_top_k(
+        ctx, last_n_tokens_data, last_n_tokens_size, top_k, top_p, temp, repeat_penalty
+    )
+
 
 def llama_print_timings(ctx: llama_context_p):
     lib.llama_print_timings(ctx)
 
+
 def llama_reset_timings(ctx: llama_context_p):
     lib.llama_reset_timings(ctx)
 
+
 def llama_print_system_info() -> bytes:
     """Print system informaiton"""
     return lib.llama_print_system_info()